139 results
Search Results
2. White paper of the Society of Abdominal Radiology hepatocellular carcinoma diagnosis disease-focused panel on LI-RADS v2018 for CT and MRI
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Elsayes, Khaled M, Kielar, Ania Z, Elmohr, Mohab M, Chernyak, Victoria, Masch, William R, Furlan, Alessandro, Marks, Robert M, Cruite, Irene, Fowler, Kathryn J, Tang, An, Bashir, Mustafa R, Hecht, Elizabeth M, Kamaya, Aya, Jambhekar, Kedar, Kamath, Amita, Arora, Sandeep, Bijan, Bijan, Ash, Ryan, Kassam, Zahra, Chaudhry, Humaira, McGahan, John P, Yacoub, Joseph H, McInnes, Matthew, Fung, Alice W, Shanbhogue, Krishna, Lee, James, Deshmukh, Sandeep, Horvat, Natally, Mitchell, Donald G, Do, Richard KG, Surabhi, Venkateswar R, Szklaruk, Janio, and Sirlin, Claude B
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Digestive Diseases ,Liver Cancer ,Cancer ,Biomedical Imaging ,Rare Diseases ,Liver Disease ,Good Health and Well Being ,Algorithms ,Carcinoma ,Hepatocellular ,Diagnosis ,Differential ,Humans ,Liver Neoplasms ,Magnetic Resonance Imaging ,Societies ,Medical ,Tomography ,X-Ray Computed ,United States ,LI-RADS ,v2018 ,CT ,MRI ,HCC - Abstract
The Liver Imaging and Reporting Data System (LI-RADS) is a comprehensive system for standardizing the terminology, technique, interpretation, reporting, and data collection of liver imaging with the overarching goal of improving communication, clinical care, education, and research relating to patients at risk for or diagnosed with hepatocellular carcinoma (HCC). In 2018, the American Association for the Study of Liver Diseases (AASLD) integrated LI-RADS into its clinical practice guidance for the imaging-based diagnosis of HCC. The harmonization between the AASLD and LI-RADS diagnostic imaging criteria required minor modifications to the recently released LI-RADS v2017 guidelines, necessitating a LI-RADS v2018 update. This article provides an overview of the key changes included in LI-RADS v2018 as well as a look at the LI-RADS v2018 diagnostic algorithm and criteria, technical recommendations, and management suggestions. Substantive changes in LI-RADS v2018 are the removal of the requirement for visibility on antecedent surveillance ultrasound for LI-RADS 5 (LR-5) categorization of 10-19 mm observations with nonrim arterial phase hyper-enhancement and nonperipheral "washout", and adoption of the Organ Procurement and Transplantation Network definition of threshold growth (≥ 50% size increase of a mass in ≤ 6 months). Nomenclatural changes in LI-RADS v2018 are the removal of -us and -g as LR-5 qualifiers.
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- 2018
3. Management of Incidental Liver Lesions on CT: A White Paper of the ACR Incidental Findings Committee
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Jeanne M. Horowitz, Richard M. Gore, Pari V. Pandharipande, Elliot K. Fishman, Mark S. Talamonti, Claus J. Fimmel, Lincoln L. Berland, Perry J. Pickhardt, and Koenraad J. Mortele
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Radiography, Abdominal ,medicine.medical_specialty ,Patient characteristics ,030218 nuclear medicine & medical imaging ,03 medical and health sciences ,0302 clinical medicine ,White paper ,Humans ,Medicine ,Radiology, Nuclear Medicine and imaging ,Practice Patterns, Physicians' ,Quality of care ,Societies, Medical ,Incidental Findings ,business.industry ,Data Collection ,Benignity ,Liver Neoplasms ,Focal nodular hyperplasia ,medicine.disease ,United States ,030220 oncology & carcinogenesis ,Hepatocellular carcinoma ,Expert opinion ,Guideline Adherence ,Radiology ,Hepatic Cyst ,Tomography, X-Ray Computed ,business ,Algorithms - Abstract
The ACR Committee on Incidental Findings presents recommendations for managing liver lesions that are incidentally detected on CT. These recommendations represent an update from the liver component of the ACR 2010 white paper on managing incidental findings in the pancreas, adrenal glands, kidneys, and liver. The Liver Subcommittee-which included five abdominal radiologists, one hepatologist, and one hepatobiliary surgeon-developed this algorithm. The recommendations draw from published evidence and expert opinion and were finalized by informal iterative consensus. Algorithm branches categorize liver lesions on the basis of patient characteristics and imaging features. They terminate with an assessment of benignity or a specific follow-up recommendation. The algorithm addresses most, but not all, pathologies and clinical scenarios. The goal is to improve the quality of care by providing guidance on how to manage incidentally detected liver lesions.
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- 2017
4. White paper of the Society of Abdominal Radiology hepatocellular carcinoma diagnosis disease-focused panel on LI-RADS v2018 for CT and MRI
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Aya Kamaya, Sandeep Deshmukh, Ryan Ash, William R. Masch, An Tang, Joseph H. Yacoub, Claude B. Sirlin, Janio Szklaruk, Natally Horvat, Victoria Chernyak, Elizabeth M. Hecht, Ania Z. Kielar, Richard K. G. Do, James T. Lee, Matthew D. F. McInnes, Sandeep Arora, John P. McGahan, Alice W. Fung, Zahra Kassam, Humaira Chaudhry, Mohab M. Elmohr, Krishna Shanbhogue, Mustafa R. Bashir, Kedar Jambhekar, Venkateswar R. Surabhi, Bijan Bijan, Irene Cruite, Amita Kamath, Robert M. Marks, Khaled M. Elsayes, Donald G. Mitchell, Alessandro Furlan, and Kathryn J. Fowler
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Disease ,030218 nuclear medicine & medical imaging ,0302 clinical medicine ,White paper ,Diagnosis ,Medicine ,HCC ,Tomography ,Societies, Medical ,Cancer ,Radiological and Ultrasound Technology ,Liver Disease ,Liver Neoplasms ,Gastroenterology ,Magnetic Resonance Imaging ,X-Ray Computed ,030220 oncology & carcinogenesis ,Hepatocellular carcinoma ,Biomedical Imaging ,LI-RADS ,Radiology ,Algorithms ,CT ,MRI ,Liver Cancer ,medicine.medical_specialty ,Carcinoma, Hepatocellular ,Urology ,MEDLINE ,Diagnosis, Differential ,03 medical and health sciences ,Rare Diseases ,Internal medicine ,Medical ,Medical imaging ,Carcinoma ,Humans ,Radiology, Nuclear Medicine and imaging ,business.industry ,Hepatocellular ,Hepatology ,medicine.disease ,United States ,Transplantation ,Differential ,v2018 ,Tomography, X-Ray Computed ,business ,Societies ,Digestive Diseases - Abstract
© 2018, Springer Science+Business Media, LLC, part of Springer Nature. The Liver Imaging and Reporting Data System (LI-RADS) is a comprehensive system for standardizing the terminology, technique, interpretation, reporting, and data collection of liver imaging with the overarching goal of improving communication, clinical care, education, and research relating to patients at risk for or diagnosed with hepatocellular carcinoma (HCC). In 2018, the American Association for the Study of Liver Diseases (AASLD) integrated LI-RADS into its clinical practice guidance for the imaging-based diagnosis of HCC. The harmonization between the AASLD and LI-RADS diagnostic imaging criteria required minor modifications to the recently released LI-RADS v2017 guidelines, necessitating a LI-RADS v2018 update. This article provides an overview of the key changes included in LI-RADS v2018 as well as a look at the LI-RADS v2018 diagnostic algorithm and criteria, technical recommendations, and management suggestions. Substantive changes in LI-RADS v2018 are the removal of the requirement for visibility on antecedent surveillance ultrasound for LI-RADS 5 (LR-5) categorization of 10-19 mm observations with nonrim arterial phase hyper-enhancement and nonperipheral “washout”, and adoption of the Organ Procurement and Transplantation Network definition of threshold growth (≥ 50% size increase of a mass in ≤ 6 months). Nomenclatural changes in LI-RADS v2018 are the removal of -us and -g as LR-5 qualifiers.
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- 2018
5. Does linear equating improve prediction in mapping? Crosswalking MacNew onto EQ-5D-5L value sets
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Admassu Nadew Lamu
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Adult ,Male ,Canada ,Mean squared error ,Cost-Benefit Analysis ,Economics, Econometrics and Finance (miscellaneous) ,Coronary Disease ,Heart disease ,03 medical and health sciences ,QALY ,0302 clinical medicine ,C1 ,Utility ,EQ-5D ,I1 ,Germany ,Equating ,Statistics ,Health Status Indicators ,Humans ,Generalizability theory ,030212 general & internal medicine ,Mathematics ,Parametric statistics ,Aged ,Original Paper ,Norway ,030503 health policy & services ,Health Policy ,Australia ,Function (mathematics) ,Middle Aged ,Economic evaluation ,United States ,Concordance correlation coefficient ,EQ-5D-5L ,Mapping ,England ,Linear Models ,Female ,Quality-Adjusted Life Years ,MacNew ,0305 other medical science ,Value (mathematics) ,Algorithms - Abstract
Purpose Preference-based measures are essential for producing quality-adjusted life years (QALYs) that are widely used for economic evaluations. In the absence of such measures, mapping algorithms can be applied to estimate utilities from disease-specific measures. This paper aims to develop mapping algorithms between the MacNew Heart Disease Quality of Life Questionnaire (MacNew) instrument and the English and the US-based EQ-5D-5L value sets. Methods Individuals with heart disease were recruited from six countries: Australia, Canada, Germany, Norway, UK and the US in 2011/12. Both parametric and non-parametric statistical techniques were applied to estimate mapping algorithms that predict utilities for MacNew scores from EQ-5D-5L value sets. The optimal algorithm for each country-specific value set was primarily selected based on root mean square error (RMSE), mean absolute error (MAE), concordance correlation coefficient (CCC), and r-squared. Leave-one-out cross-validation was conducted to test the generalizability of each model. Results For both the English and the US value sets, the one-inflated beta regression model consistently performed best in terms of all criteria. Similar results were observed for the cross-validation results. The preferred model explained 59 and 60% for the English and the US value set, respectively. Linear equating provided predicted values that were equivalent to observed values. Conclusions The preferred mapping function enables to predict utilities for MacNew data from the EQ-5D-5L value sets recently developed in England and the US with better accuracy. This allows studies, which have included the MacNew to be used in cost-utility analyses and thus, the comparison of services with interventions across the health system.
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- 2020
6. Relative Reduction in Prevalence (RRP): An Alternative to Cohen’s Effect Size Statistics for Judging Alcohol, Cigarette, and Marijuana Use Prevention Outcomes
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AlessanRSS Reis and William Hansen
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Male ,medicine.medical_specialty ,Adolescent ,Alcohol Drinking ,Effect size ,Marijuana Smoking ,Smoking Prevention ,Intervention effect ,03 medical and health sciences ,0302 clinical medicine ,Marijuana use ,Intervention (counseling) ,Prevalence ,Humans ,Medicine ,030212 general & internal medicine ,Cigarette ,Evaluation ,Students ,Alternative methods ,Original Paper ,business.industry ,Prevention ,Public health ,Smoking ,05 social sciences ,Public Health, Environmental and Occupational Health ,050301 education ,United States ,Marijuana ,Health psychology ,Survey data collection ,Female ,Alcohol ,business ,0503 education ,Algorithms ,Program Evaluation ,Clinical psychology - Abstract
Jacob Cohen developed two statistical measures for judging the magnitude of effects produced by an intervention, known as Cohen’s d, appropriate for assessing scaled data, and Cohen’s h, appropriate for assessing proportions. These have been widely employed in evaluating the effectiveness of alcohol, cigarette, marijuana, and other drug prevention efforts. I present two tests to consider the adequacy of using these statistics when applied to drug use prevention programs. I used student survey data from grades 6 through 12 (N = 1,963,964) collected by the Georgia Department of Education between 2015 and 2017 and aggregated at the school level (N = 1036). I calculated effect sizes for an imaginary drug prevention program that (1) reduced 30-day alcohol, cigarette, and marijuana prevalence by 50%; and (2) maintained 30-day prevalence at a pretest level for multiple years. While both approaches to estimating intervention effects represent ideal outcomes for prevention that surpass what is normally observed, Cohen’s statistics failed to reflect the effectiveness of these approaches. I recommend including an alternative method for calculating effect size for judging program outcomes. This alternative method, Relative Reduction in Prevalence (RRP), calculates ratio differences between treatment and control group drug use prevalence at posttest and follow-up, adjusting for differences observed at pretest. RRP allows researchers to state the degree to which an intervention could be viewed as efficacious or effective that can be readily understood by practitioners.
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- 2020
7. Pattern Recognition to Identify Stroke in the Cognitive Profile: Secondary Analyses of a Prospective Cohort Study
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Sean A. P. Clouston, Dylan M. Smith, and Yun Zhang
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Male ,lcsh:Diseases of the circulatory (Cardiovascular) system ,Time Factors ,Memory, Episodic ,Population ,Neuropsychological Tests ,030204 cardiovascular system & hematology ,Pattern Recognition, Automated ,03 medical and health sciences ,Cognition ,0302 clinical medicine ,Predictive Value of Tests ,neuroepidemiology ,medicine ,Humans ,Cognitive Dysfunction ,Prospective Studies ,cardiovascular diseases ,Cognitive decline ,education ,Prospective cohort study ,Episodic memory ,Stroke ,Aged ,adaptive diagnostics ,Original Paper ,education.field_of_study ,business.industry ,Incidence ,Incidence (epidemiology) ,pattern recognition ,Pattern recognition ,Secondary data ,Middle Aged ,medicine.disease ,United States ,Neurology ,lcsh:RC666-701 ,Female ,Neurology (clinical) ,Artificial intelligence ,Cardiology and Cardiovascular Medicine ,business ,Algorithms ,030217 neurology & neurosurgery ,cerebrovascular diseases - Abstract
Background: Stroke can produce subtle changes in the brain that may produce symptoms that are too small to lead to a diagnosis. Noting that a lack of diagnosis may bias research estimates, the current study sought to examine the utility of pattern recognition relying on serial assessments of cognition to objectively identify stroke-like patterns of cognitive decline (pattern-detected stroke, p-stroke). Methods: Secondary data analysis was conducted using participants with no reported history of stroke in the Health and Retirement Study, a large (n = 16,113) epidemiological study of cognitive aging among respondents aged 50 years and older that measured episodic memory consistently biennially between 1996 and 2014. Analyses were limited to participants with at least 4 serial measures of episodic memory. Occurrence and date of p-stroke events were identified utilizing pattern recognition to identify stepwise declines in cognition consistent with stroke. Descriptive statistics included the percentage of the population with p-stroke, the mean change in episodic memory resulting in stroke-positive testing, and the mean time between p-stroke and first major diagnosed stroke. Statistical analyses comparing cases of p-stroke with reported major stroke relied on the area under the receiver-operating curve (AUC). Longitudinal modeling was utilized to examine rates of change in those with/without major stroke after adjusting for demographics. Results: The pattern recognition protocol identified 7,499 p-strokes that went unreported. On average, individuals with p-stroke declined in episodic memory by 1.986 (SD = 0.023) words at the inferred time of stroke. The resulting pattern recognition protocol was able to identify self-reported major stroke (AUC = 0.58, 95% CI = 0.57–0.59, p < 0.001). In those with a reported major stroke, p-stroke events were detectable on average 4.963 (4.650–5.275) years (p < 0.001) before diagnosis was first reported. The incidence of p-stroke was 40.23/1,000 (95% CI = 39.40–41.08) person-years. After adjusting for sex, age was associated with the incidence of p-stroke and major stroke at similar rates. Conclusions: This is the first study to propose utilizing pattern recognition to identify the incidence and timing of p-stroke. Further work is warranted examining the clinical utility of pattern recognition in identifying p-stroke in longitudinal cognitive profiles.
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- 2019
8. Combating Health Care Fraud and Abuse: Conceptualization and Prototyping Study of a Blockchain Antifraud Framework
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Ken Miyachi, Tim K. Mackey, James Short, Danny Fung, and Samson Qian
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blockchain ,Blockchain ,020205 medical informatics ,Smart contract ,Computer science ,Concept Formation ,Data management ,Health Informatics ,02 engineering and technology ,Medicare ,Computer security ,computer.software_genre ,lcsh:Computer applications to medicine. Medical informatics ,Health informatics ,03 medical and health sciences ,0302 clinical medicine ,Health care ,0202 electrical engineering, electronic engineering, information engineering ,Humans ,medical informatics ,030212 general & internal medicine ,Original Paper ,business.industry ,lcsh:Public aspects of medicine ,Fraud ,lcsh:RA1-1270 ,information science ,United States ,Digital identity ,Workflow ,delivery of healthcare ,lcsh:R858-859.7 ,business ,Medicaid ,computer ,Algorithms - Abstract
Background An estimated US $2.6 billion loss is attributed to health care fraud and abuse. With traditional health care claims verification and reimbursement, the health care provider submits a claim after rendering services to a patient, which is then verified and reimbursed by the payer. However, this process leaves out a critical stakeholder: the patient for whom the services are actually rendered. This lack of patient participation introduces a risk of fraud and abuse. Blockchain technology enables secure data management with transparency, which could mitigate this risk of health care fraud and abuse. Objective The aim of this study is to develop a framework using blockchain to record claims data and transactions in an immutable format and to enable the patient to act as a validating node to help detect and prevent health care fraud and abuse. Methods We developed a health care fraud and abuse blockchain technical framework and prototype using key blockchain tools and application layers including consensus algorithms, smart contracts, tokens, and governance based on digital identity on the Ethereum platform (Ethereum Foundation). Results Our technical framework maps to the claims adjudication process and focuses on Medicare claims, with the US Centers for Medicare and Medicaid Services (CMS) as the central authority. A prototype of the framework system was developed using the blockchain platform Ethereum (Ethereum Foundation), with its design features, workflow, smart contract functions, system architecture, and software implementation outlined. The software stack used to build the system consisted of a front-end user interface framework, a back-end processing server, and a blockchain network. React was used for the user interface framework, and NodeJS and an Express server were used for the back-end processing server; Solidity was the smart contract language used to interact with a local Ethereum blockchain network. Conclusions The proposed framework and the initial prototype have the potential to improve the health care claims process by using blockchain technology for secure data storage and consensus mechanisms, which make the claims adjudication process more patient-centric for the purposes of identifying and preventing health care fraud and abuse. Future work will focus on the use of synthetic or historic CMS claims data to assess the real-world viability of the framework.
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- 2020
9. An Automated Approach for Finding Spatio-Temporal Patterns of Seasonal Influenza in the United States: Algorithm Validation Study
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Prathyush Sambaturu, Bryan Lewis, Madhav V. Marathe, Jiangzhuo Chen, Parantapa Bhattacharya, Anil Vullikanti, and Srinivasan Venkatramanan
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Computer science ,Health Informatics ,Context (language use) ,02 engineering and technology ,Validation Studies as Topic ,computer.software_genre ,Set (abstract data type) ,transactional data mining ,Spatio-Temporal Analysis ,020204 information systems ,Influenza, Human ,0202 electrical engineering, electronic engineering, information engineering ,Data Mining ,Humans ,Minimum description length ,Integer programming ,Incidence (geometry) ,Original Paper ,Public Health, Environmental and Occupational Health ,summarization ,Automatic summarization ,epidemic data analysis ,United States ,spatio-temporal patterns ,Unsupervised learning ,020201 artificial intelligence & image processing ,Data mining ,Seasons ,Transaction data ,computer ,Algorithms - Abstract
Background Agencies such as the Centers for Disease Control and Prevention (CDC) currently release influenza-like illness incidence data, along with descriptive summaries of simple spatio-temporal patterns and trends. However, public health researchers, government agencies, as well as the general public, are often interested in deeper patterns and insights into how the disease is spreading, with additional context. Analysis by domain experts is needed for deriving such insights from incidence data. Objective Our goal was to develop an automated approach for finding interesting spatio-temporal patterns in the spread of a disease over a large region, such as regions which have specific characteristics (eg, high incidence in a particular week, those which showed a sudden change in incidence) or regions which have significantly different incidence compared to earlier seasons. Methods We developed techniques from the area of transactional data mining for characterizing and finding interesting spatio-temporal patterns in disease spread in an automated manner. A key part of our approach involved using the principle of minimum description length for representing a given target set in terms of combinations of attributes (referred to as clauses); we considered both positive and negative clauses, relaxed descriptions which approximately represent the set, and used integer programming to find such descriptions. Finally, we designed an automated approach, which examines a large space of sets corresponding to different spatio-temporal patterns, and ranks them based on the ratio of their size to their description length (referred to as the compression ratio). Results We applied our methods using minimum description length to find spatio-temporal patterns in the spread of seasonal influenza in the United States using state level influenza-like illness activity indicator data from the CDC. We observed that the compression ratios were over 2.5 for 50% of the chosen sets, when approximate descriptions and negative clauses were allowed. Sets with high compression ratios (eg, over 2.5) corresponded to interesting patterns in the spatio-temporal dynamics of influenza-like illness. Our approach also outperformed description by solution in terms of the compression ratio. Conclusions Our approach, which is an unsupervised machine learning method, can provide new insights into patterns and trends in the disease spread in an automated manner. Our results show that the description complexity is an effective approach for characterizing sets of interest, which can be easily extended to other diseases and regions beyond influenza in the US. Our approach can also be easily adapted for automated generation of narratives.
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- 2020
10. Early Stage Machine Learning–Based Prediction of US County Vulnerability to the COVID-19 Pandemic: Machine Learning Approach
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Mihir Mehta, Juxihong Julaiti, Soundar R. T. Kumara, and Paul M. Griffin
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020205 medical informatics ,Coronavirus disease 2019 (COVID-19) ,Urban Population ,Population ,Pneumonia, Viral ,Vulnerability ,coronavirus ,Health Informatics ,02 engineering and technology ,Comorbidity ,Machine learning ,computer.software_genre ,Risk Assessment ,Machine Learning ,03 medical and health sciences ,Betacoronavirus ,0302 clinical medicine ,Pandemic ,0202 electrical engineering, electronic engineering, information engineering ,Humans ,030212 general & internal medicine ,education ,Pandemics ,Aged ,county-level vulnerability ,Population Density ,education.field_of_study ,Government ,Original Paper ,Models, Statistical ,business.industry ,SARS-CoV-2 ,Public Health, Environmental and Occupational Health ,COVID-19 ,United States ,prediction model ,Identification (information) ,Geography ,Population Surveillance ,Stage (hydrology) ,Artificial intelligence ,Public aspects of medicine ,RA1-1270 ,Risk assessment ,business ,Coronavirus Infections ,computer ,Algorithms ,XGBoost - Abstract
Background The rapid spread of COVID-19 means that government and health services providers have little time to plan and design effective response policies. It is therefore important to quickly provide accurate predictions of how vulnerable geographic regions such as counties are to the spread of this virus. Objective The aim of this study is to develop county-level prediction around near future disease movement for COVID-19 occurrences using publicly available data. Methods We estimated county-level COVID-19 occurrences for the period March 14 to 31, 2020, based on data fused from multiple publicly available sources inclusive of health statistics, demographics, and geographical features. We developed a three-stage model using XGBoost, a machine learning algorithm, to quantify the probability of COVID-19 occurrence and estimate the number of potential occurrences for unaffected counties. Finally, these results were combined to predict the county-level risk. This risk was then used as an estimated after-five-day-vulnerability of the county. Results The model predictions showed a sensitivity over 71% and specificity over 94% for models built using data from March 14 to 31, 2020. We found that population, population density, percentage of people aged >70 years, and prevalence of comorbidities play an important role in predicting COVID-19 occurrences. We observed a positive association at the county level between urbanicity and vulnerability to COVID-19. Conclusions The developed model can be used for identification of vulnerable counties and potential data discrepancies. Limited testing facilities and delayed results introduce significant variation in reported cases, which produces a bias in the model.
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- 2020
11. Regional Infoveillance of COVID-19 Case Rates: Analysis of Search-Engine Query Patterns
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Henry C. Cousins, Alon Harris, Clara C. Cousins, and Louis R. Pasquale
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020205 medical informatics ,Coronavirus disease 2019 (COVID-19) ,Mean squared error ,infectious disease ,Pneumonia, Viral ,Google Trends ,Health Informatics ,02 engineering and technology ,lcsh:Computer applications to medicine. Medical informatics ,03 medical and health sciences ,symbols.namesake ,Betacoronavirus ,infoveillance ,0302 clinical medicine ,Statistical significance ,Statistics ,0202 electrical engineering, electronic engineering, information engineering ,Humans ,030212 general & internal medicine ,internet activity ,Pandemics ,Mathematics ,Public Health Informatics ,Internet ,Original Paper ,Web search query ,Models, Statistical ,SARS-CoV-2 ,lcsh:Public aspects of medicine ,public health ,Univariate ,COVID-19 ,lcsh:RA1-1270 ,Regression ,Pearson product-moment correlation coefficient ,United States ,Search Engine ,Infoveillance ,Population Surveillance ,symbols ,surveillance ,lcsh:R858-859.7 ,epidemiology ,Coronavirus Infections ,Algorithms - Abstract
Background Timely allocation of medical resources for coronavirus disease (COVID-19) requires early detection of regional outbreaks. Internet browsing data may predict case outbreaks in local populations that are yet to be confirmed. Objective We investigated whether search-engine query patterns can help to predict COVID-19 case rates at the state and metropolitan area levels in the United States. Methods We used regional confirmed case data from the New York Times and Google Trends results from 50 states and 166 county-based designated market areas (DMA). We identified search terms whose activity precedes and correlates with confirmed case rates at the national level. We used univariate regression to construct a composite explanatory variable based on best-fitting search queries offset by temporal lags. We measured the raw and z-transformed Pearson correlation and root-mean-square error (RMSE) of the explanatory variable with out-of-sample case rate data at the state and DMA levels. Results Predictions were highly correlated with confirmed case rates at the state (mean r=0.69, 95% CI 0.51-0.81; median RMSE 1.27, IQR 1.48) and DMA levels (mean r=0.51, 95% CI 0.39-0.61; median RMSE 4.38, IQR 1.80), using search data available up to 10 days prior to confirmed case rates. They fit case-rate activity in 49 of 50 states and in 103 of 166 DMA at a significance level of .05. Conclusions Identifiable patterns in search query activity may help to predict emerging regional outbreaks of COVID-19, although they remain vulnerable to stochastic changes in search intensity.
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- 2020
12. A practical and adaptive approach to lung cancer screening: a review of international evidence and position on CT lung cancer screening in the Singaporean population by the College of Radiologists Singapore
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Haja Mohamed Mohideen Salahudeen, Andrew Gee Seng Tan, Hong Chou, Angeline Choo Choo Poh, Lynette Teo, Augustine Kim Huat Tee, Lester Chee Hao Leong, Charlene Jin Yee Liew, Foong Koon Cheah, B. Tan, Ian Yu Yan Tsou, Kiang Hiong Tay, Wei Ping Tham, Ching Ching Ong, Gregory Kaw, Raymond Quah, Daniel Tan, and Chau Hung Lee
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medicine.medical_specialty ,Lung Neoplasms ,Cost-Benefit Analysis ,Population ,Review Article ,030204 cardiovascular system & hematology ,Radiation Dosage ,Risk Assessment ,03 medical and health sciences ,0302 clinical medicine ,Deep Learning ,Epidemiology ,medicine ,Humans ,Mass Screening ,False Positive Reactions ,030212 general & internal medicine ,Diagnosis, Computer-Assisted ,Registries ,education ,Lung cancer ,Early Detection of Cancer ,education.field_of_study ,Clinical Trials as Topic ,Singapore ,business.industry ,Public health ,General Medicine ,medicine.disease ,United States ,Review article ,Europe ,Family medicine ,Radiological weapon ,Practice Guidelines as Topic ,Position paper ,Interdisciplinary Communication ,Smoking Cessation ,Public Health ,business ,Radiology ,Tomography, X-Ray Computed ,Lung cancer screening ,Algorithms - Abstract
Lung cancer is the leading cause of cancer-related death around the world, being the top cause of cancer-related deaths among men and the second most common cause of cancer-related deaths among women in Singapore. Currently, no screening programme for lung cancer exists in Singapore. Since there is mounting evidence indicating a different epidemiology of lung cancer in Asian countries, including Singapore, compared to the rest of the world, a unique and adaptive approach must be taken for a screening programme to be successful at reducing mortality while maintaining cost-effectiveness and a favourable risk-benefit ratio. This review article promotes the use of low-dose computed tomography of the chest and explores the radiological challenges and future directions.
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- 2019
13. Multi-step prediction for influenza outbreak by an adjusted long short-term memory
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Kazumitsu Nawata and J. Zhang
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0301 basic medicine ,Time Factors ,Epidemiology ,Influenza vaccine ,Computer science ,Influenza-like illness (ILI) ,Influenza season ,02 engineering and technology ,Disease Outbreaks ,03 medical and health sciences ,Long short term memory ,Statistics ,Influenza, Human ,0202 electrical engineering, electronic engineering, information engineering ,Humans ,Influenza outbreak ,Original Paper ,long short-term memory (LSTM) ,Outbreak ,Influenza ,United States ,Prediction algorithms ,030104 developmental biology ,Infectious Diseases ,multi-step-ahead time-series prediction ,020201 artificial intelligence & image processing ,Algorithms ,Forecasting - Abstract
Influenza results in approximately 3–5 million annual cases of severe illness and 250 000–500 000 deaths. We urgently need an accurate multi-step-ahead time-series forecasting model to help hospitals to perform dynamical assignments of beds to influenza patients for the annually varied influenza season, and aid pharmaceutical companies to formulate a flexible plan of manufacturing vaccine for the yearly different influenza vaccine. In this study, we utilised four different multi-step prediction algorithms in the long short-term memory (LSTM). The result showed that implementing multiple single-output prediction in a six-layer LSTM structure achieved the best accuracy. The mean absolute percentage errors from two- to 13-step-ahead prediction for the US influenza-like illness rates were all
- Published
- 2018
14. Automated Travel History Extraction From Clinical Notes for Informing the Detection of Emergent Infectious Disease Events: Algorithm Development and Validation
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Vanessa W. Stevens, Kelly S. Peterson, Olga V. Patterson, Alec B. Chapman, Makoto Jones, Katherine S. Wallace, Gary A. Roselle, Julia Lewis, Patricia A Lye, Daniel W. Denhalter, and Shantini D. Gamage
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biosurveillance ,Male ,medicine.medical_specialty ,020205 medical informatics ,Computer science ,Information Storage and Retrieval ,Health Informatics ,02 engineering and technology ,infectious disease surveillance ,Communicable Diseases, Emerging ,surveillance applications ,Machine Learning ,03 medical and health sciences ,Zika ,0302 clinical medicine ,Documentation ,Cohen's kappa ,Text processing ,Health care ,0202 electrical engineering, electronic engineering, information engineering ,medicine ,Electronic Health Records ,Humans ,Public Health Surveillance ,030212 general & internal medicine ,natural language processing ,Original Paper ,Travel ,business.industry ,Public health ,Public Health, Environmental and Occupational Health ,COVID-19 ,Reproducibility of Results ,electronic health record ,Middle Aged ,Data science ,United States ,Infectious disease (medical specialty) ,travel history ,Preparedness ,Feasibility Studies ,Female ,Language model ,Public aspects of medicine ,RA1-1270 ,business ,Algorithms - Abstract
BackgroundPatient travel history can be crucial in evaluating evolving infectious disease events. Such information can be challenging to acquire in electronic health records, as it is often available only in unstructured text.ObjectiveThis study aims to assess the feasibility of annotating and automatically extracting travel history mentions from unstructured clinical documents in the Department of Veterans Affairs across disparate health care facilities and among millions of patients. Information about travel exposure augments existing surveillance applications for increased preparedness in responding quickly to public health threats.MethodsClinical documents related to arboviral disease were annotated following selection using a semiautomated bootstrapping process. Using annotated instances as training data, models were developed to extract from unstructured clinical text any mention of affirmed travel locations outside of the continental United States. Automated text processing models were evaluated, involving machine learning and neural language models for extraction accuracy.ResultsAmong 4584 annotated instances, 2659 (58%) contained an affirmed mention of travel history, while 347 (7.6%) were negated. Interannotator agreement resulted in a document-level Cohen kappa of 0.776. Automated text processing accuracy (F1 85.6, 95% CI 82.5-87.9) and computational burden were acceptable such that the system can provide a rapid screen for public health events.ConclusionsAutomated extraction of patient travel history from clinical documents is feasible for enhanced passive surveillance public health systems. Without such a system, it would usually be necessary to manually review charts to identify recent travel or lack of travel, use an electronic health record that enforces travel history documentation, or ignore this potential source of information altogether. The development of this tool was initially motivated by emergent arboviral diseases. More recently, this system was used in the early phases of response to COVID-19 in the United States, although its utility was limited to a relatively brief window due to the rapid domestic spread of the virus. Such systems may aid future efforts to prevent and contain the spread of infectious diseases.
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- 2021
15. Association between Benzodiazepine Use and Dementia: Data Mining of Different Medical Databases
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Mitsutaka Takada, Mai Fujimoto, and Kouichi Hosomi
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Canada ,Databases, Factual ,medicine.drug_class ,media_common.quotation_subject ,computer.software_genre ,Drug Prescriptions ,Risk Assessment ,Benzodiazepines ,03 medical and health sciences ,Adverse Event Reporting System ,0302 clinical medicine ,Odds Ratio ,medicine ,Adverse Drug Reaction Reporting Systems ,Data Mining ,Humans ,Dementia ,030212 general & internal medicine ,Medical prescription ,Adverse effect ,Aged ,media_common ,Benzodiazepine ,Database ,United States Food and Drug Administration ,business.industry ,General Medicine ,Odds ratio ,medicine.disease ,United States ,Anti-Anxiety Agents ,Data mining ,Risk assessment ,business ,computer ,Algorithms ,030217 neurology & neurosurgery ,Research Paper ,Vigilance (psychology) - Abstract
Purpose: Some studies have suggested that the use of benzodiazepines in the elderly is associated with an increased risk of dementia. However, this association might be due to confounding by indication and reverse causation. To examine the association between benzodiazepine anxiolytic drug use and the risk of dementia, we conducted data mining of a spontaneous reporting database and a large organized database of prescriptions. Methods: Data from the US Food and Drug Administration Adverse Event Reporting System (FAERS) from the first quarter of 2004 through the end of 2013 and data from the Canada Vigilance Adverse Reaction Online Database from the first quarter of 1965 through the end of 2013 were used for the analyses. The reporting odds ratio (ROR) and information component (IC) were calculated. In addition, prescription sequence symmetry analysis (PSSA) was performed to identify the risk of dementia after using benzodiazepine anxiolytic drugs over the period of January 2006 to May 2014. Results: Benzodiazepine use was found to be associated with dementia in analyses using the FAERS database (ROR: 1.63, 95% CI: 1.61-1.64; IC: 0.66, 95% CI: 0.65-0.67) and the Canada Vigilance Adverse Reaction Online Database (ROR: 1.88, 95% CI: 1.83-1.94; IC: 0.85, 95% CI: 0.80-0.89). ROR and IC values increased with the duration of action of benzodiazepines. In the PSSA, a significant association was found, with adjusted sequence ratios of 1.24 (1.05-1.45), 1.20 (1.06-1.37), 1.23 (1.11-1.37), 1.34 (1.23-1.47), 1.41 (1.29-1.53), and 1.44 (1.33-1.56) at intervals of 3, 6, 12, 24, 36, and 48 months, respectively. Furthermore, the additional PSSA, in which patients who initiated a new treatment with benzodiazepines and anti-dementia drugs within 12- and 24-month periods were excluded from the analysis, demonstrated significant associations of benzodiazepine use with dementia risk. Conclusion: Multi-methodological approaches using different methods, algorithms, and databases suggest that long-term use of benzodiazepines and long-acting benzodiazepines are strongly associated with an increased risk of dementia.
- Published
- 2016
16. The Effect of Excipients on the Permeability of BCS Class III Compounds and Implications for Biowaivers
- Author
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Mario A. Gonzalez, Chris Bode, Wenlei Jiang, Kevin Miller, Ismael J. Hidalgo, Alan F. Parr, William Brown, Mehran Yazdanian, Kazuko Sagawa, and Erika S. Stippler
- Subjects
Therapeutic equivalency ,BCS class III ,Pharmaceutical Science ,02 engineering and technology ,Class iii ,030226 pharmacology & pharmacy ,Intestinal absorption ,Permeability ,Biopharmaceutics ,Excipients ,Rats, Sprague-Dawley ,03 medical and health sciences ,Surface-Active Agents ,0302 clinical medicine ,Computational chemistry ,Low permeability ,Animals ,Humans ,Pharmacology (medical) ,Solubility ,Pharmacology ,Chromatography ,Chemistry ,United States Food and Drug Administration ,Organic Chemistry ,Sodium Dodecyl Sulfate ,Caco-2 ,021001 nanoscience & nanotechnology ,Biopharmaceutics Classification System ,United States ,Rats ,Permeability (earth sciences) ,Jejunum ,Intestinal Absorption ,Therapeutic Equivalency ,Molecular Medicine ,Caco-2 Cells ,0210 nano-technology ,bioavailability ,rat intestinal perfusion model ,Algorithms ,Biotechnology ,Research Paper - Abstract
Purpose Currently, the FDA allows biowaivers for Class I (high solubility and high permeability) and Class III (high solubility and low permeability) compounds of the Biopharmaceutics Classification System (BCS). Scientific evidence should be provided to support biowaivers for BCS Class I and Class III (high solubility and low permeability) compounds. Methods Data on the effects of excipients on drug permeability are needed to demonstrate that commonly used excipients do not affect the permeability of BCS Class III compounds, which would support the application of biowaivers to Class III compounds. This study was designed to generate such data by assessing the permeability of four BCS Class III compounds and one Class I compound in the presence and absence of five commonly used excipients. Results The permeability of each of the compounds was assessed, at three to five concentrations, with each excipient in two different models: Caco-2 cell monolayers, and in situ rat intestinal perfusion. No substantial increases in the permeability of any of the compounds were observed in the presence of any of the tested excipients in either of the models, with the exception of disruption of Caco-2 cell monolayer integrity by sodium lauryl sulfate at 0.1 mg/ml and higher. Conclusion The results suggest that the absorption of these four BCS Class III compounds would not be greatly affected by the tested excipients. This may have implications in supporting biowaivers for BCS Class III compounds in general.
- Published
- 2015
17. A personalized channel recommendation and scheduling system considering both section video clips and full video clips.
- Author
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Lee, SeungGwan and Lee, DaeHo
- Subjects
BROADCASTING industry ,VIDEO excerpts ,TECHNOLOGY convergence ,RECOMMENDER systems ,PREDICTION theory - Abstract
With the convergence of various broadcasting systems, the amount of content available in mobile terminals including IPTV has significantly increased. In this paper, we propose a system that enables users to schedule programs considering both section video clips and full video clips based on the user detection method with similar preference. And, since the system constituting the contents can be classified according to the program, the proposed method can store a program desired by the user, and thus create and schedule a kind of individual channel. Experimental results show that the proposed method has a higher prediction accuracy; this is accomplished by comparing existing channel recommendation methods with the program recommendation methods proposed in this paper. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
18. Dynamics in the Fitness-Income plane: Brazilian states vs World countries.
- Author
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Operti, Felipe G., Pugliese, Emanuele, Jr.Andrade, José S., Pietronero, Luciano, and Gabrielli, Andrea
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ALGORITHMS ,PHYSICAL fitness ,GROSS domestic product ,ECONOMICS - Abstract
In this paper we introduce a novel algorithm, called Exogenous Fitness, to calculate the Fitness of subnational entities and we apply it to the states of Brazil. In the last decade, several indices were introduced to measure the competitiveness of countries by looking at the complexity of their export basket. Tacchella et al (2012) developed a non-monetary metric called Fitness. In this paper, after an overview about Brazil as a whole and the comparison with the other BRIC countries, we introduce a new methodology based on the Fitness algorithm, called Exogenous Fitness. Combining the results with the Gross Domestic Product per capita (GDP
p ), we look at the dynamics of the Brazilian states in the Fitness-Income plane. Two regimes are distinguishable: one with high predictability and the other with low predictability, showing a deep analogy with the heterogeneous dynamics of the World countries. Furthermore, we compare the ranking of the Brazilian states according to the Exogenous Fitness with the ranking obtained through two other techniques, namely Endogenous Fitness and Economic Complexity Index. [ABSTRACT FROM AUTHOR]- Published
- 2018
- Full Text
- View/download PDF
19. Reliable Facility Location Problem with Facility Protection.
- Author
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Tang, Luohao, Zhu, Cheng, Lin, Zaili, Shi, Jianmai, and Zhang, Weiming
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LOCATION problems (Programming) ,FACILITY location problems ,INTEGER programming ,LAGRANGIAN functions ,APPLIED mathematics - Abstract
This paper studies a reliable facility location problem with facility protection that aims to hedge against random facility disruptions by both strategically protecting some facilities and using backup facilities for the demands. An Integer Programming model is proposed for this problem, in which the failure probabilities of facilities are site-specific. A solution approach combining Lagrangian Relaxation and local search is proposed and is demonstrated to be both effective and efficient based on computational experiments on random numerical examples with 49, 88, 150 and 263 nodes in the network. A real case study for a 100-city network in Hunan province, China, is presented, based on which the properties of the model are discussed and some managerial insights are analyzed. [ABSTRACT FROM AUTHOR]
- Published
- 2016
- Full Text
- View/download PDF
20. Using Tensor Completion Method to Achieving Better Coverage of Traffic State Estimation from Sparse Floating Car Data.
- Author
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Ran, Bin, Song, Li, Zhang, Jian, Cheng, Yang, and Tan, Huachun
- Subjects
TRAFFIC engineering ,ESTIMATION theory ,PROBLEM solving ,STATISTICAL correlation ,MISSING data (Statistics) - Abstract
Traffic state estimation from the floating car system is a challenging problem. The low penetration rate and random distribution make available floating car samples usually cover part space and time points of the road networks. To obtain a wide range of traffic state from the floating car system, many methods have been proposed to estimate the traffic state for the uncovered links. However, these methods cannot provide traffic state of the entire road networks. In this paper, the traffic state estimation is transformed to solve a missing data imputation problem, and the tensor completion framework is proposed to estimate missing traffic state. A tensor is constructed to model traffic state in which observed entries are directly derived from floating car system and unobserved traffic states are modeled as missing entries of constructed tensor. The constructed traffic state tensor can represent spatial and temporal correlations of traffic data and encode the multi-way properties of traffic state. The advantage of the proposed approach is that it can fully mine and utilize the multi-dimensional inherent correlations of traffic state. We tested the proposed approach on a well calibrated simulation network. Experimental results demonstrated that the proposed approach yield reliable traffic state estimation from very sparse floating car data, particularly when dealing with the floating car penetration rate is below 1%. [ABSTRACT FROM AUTHOR]
- Published
- 2016
- Full Text
- View/download PDF
21. Best Match: New relevance search for PubMed.
- Author
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Fiorini, Nicolas, Canese, Kathi, Starchenko, Grisha, Kireev, Evgeny, Kim, Won, Miller, Vadim, Osipov, Maxim, Kholodov, Michael, Ismagilov, Rafis, Mohan, Sunil, Ostell, James, and Lu, Zhiyong
- Subjects
SEARCH engines ,SEARCH algorithms ,INTERNET searching ,DATA mining ,MEDICAL literature - Abstract
PubMed is a free search engine for biomedical literature accessed by millions of users from around the world each day. With the rapid growth of biomedical literature—about two articles are added every minute on average—finding and retrieving the most relevant papers for a given query is increasingly challenging. We present Best Match, a new relevance search algorithm for PubMed that leverages the intelligence of our users and cutting-edge machine-learning technology as an alternative to the traditional date sort order. The Best Match algorithm is trained with past user searches with dozens of relevance-ranking signals (factors), the most important being the past usage of an article, publication date, relevance score, and type of article. This new algorithm demonstrates state-of-the-art retrieval performance in benchmarking experiments as well as an improved user experience in real-world testing (over 20% increase in user click-through rate). Since its deployment in June 2017, we have observed a significant increase (60%) in PubMed searches with relevance sort order: it now assists millions of PubMed searches each week. In this work, we hope to increase the awareness and transparency of this new relevance sort option for PubMed users, enabling them to retrieve information more effectively. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
22. Capturing the influence of geopolitical ties from Wikipedia with reduced Google matrix.
- Author
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El Zant, Samer, Jaffrès-Runser, Katia, and Shepelyansky, Dima L.
- Subjects
SOCIOCULTURAL factors ,GEOPOLITICS ,POWER (Social sciences) ,MARKOV processes - Abstract
Interactions between countries originate from diverse aspects such as geographic proximity, trade, socio-cultural habits, language, religions, etc. Geopolitics studies the influence of a country’s geographic space on its political power and its relationships with other countries. This work reveals the potential of Wikipedia mining for geopolitical study. Actually, Wikipedia offers solid knowledge and strong correlations among countries by linking web pages together for different types of information (e.g. economical, historical, political, and many others). The major finding of this paper is to show that meaningful results on the influence of country ties can be extracted from the hyperlinked structure of Wikipedia. We leverage a novel stochastic matrix representation of Markov chains of complex directed networks called the reduced Google matrix theory. For a selected small size set of nodes, the reduced Google matrix concentrates direct and indirect links of the million-node sized Wikipedia network into a small Perron-Frobenius matrix keeping the PageRank probabilities of the global Wikipedia network. We perform a novel sensitivity analysis that leverages this reduced Google matrix to characterize the influence of relationships between countries from the global network. We apply this analysis to two chosen sets of countries (i.e. the set of 27 European Union countries and a set of 40 top worldwide countries). We show that with our sensitivity analysis we can exhibit easily very meaningful information on geopolitics from five different Wikipedia editions (English, Arabic, Russian, French and German). [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
23. Accurate and fast path computation on large urban road networks: A general approach.
- Author
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Song, Qing, Li, Meng, and Li, Xiaolei
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TRANSPORTATION ,TRAFFIC engineering ,ROADS ,NAVIGATION ,ALGORITHMS - Abstract
Accurate and fast path computation is essential for applications such as onboard navigation systems and traffic network routing. While a number of heuristic algorithms have been developed in the past few years for faster path queries, the accuracy of them are always far below satisfying. In this paper, we first develop an agglomerative graph partitioning method for generating high balanced traverse distance partitions, and we constitute a three-level graph model based on the graph partition scheme for structuring the urban road network. Then, we propose a new hierarchical path computation algorithm, which benefits from the hierarchical graph model and utilizes a region pruning strategy to significantly reduce the search space without compromising the accuracy. Finally, we present a detailed experimental evaluation on the real urban road network of New York City, and the experimental results demonstrate the effectiveness of the proposed approach to generate optimal fast paths and to facilitate real-time routing applications. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
24. What quantifies good primary care in the United States? A review of algorithms and metrics using real-world data.
- Author
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Wang, Yun, Zheng, Jianwei, Schneberk, Todd, Ke, Yu, Chan, Alexandre, Hu, Tao, Lam, Jerika, Gutierrez, Mary, Portillo, Ivan, Wu, Dan, Chang, Chih-Hung, Qu, Yang, Brown, Lawrence, and Nichol, Michael B
- Subjects
Humans ,Algorithms ,Benchmarking ,Primary Health Care ,United States ,Surveys and Questionnaires ,COVID-19 ,Claims data ,Electronic health records ,Metrics ,Primary care ,Quality ,Health Services ,Patient Safety ,Clinical Research ,Networking and Information Technology R&D (NITRD) ,8.1 Organisation and delivery of services ,Health and social care services research ,Generic health relevance ,Good Health and Well Being - Abstract
Primary care physicians (PCPs) play an indispensable role in providing comprehensive care and referring patients for specialty care and other medical services. As the COVID-19 outbreak disrupts patient access to care, understanding the quality of primary care is critical at this unprecedented moment to support patients with complex medical needs in the primary care setting and inform policymakers to redesign our primary care system. The traditional way of collecting information from patient surveys is time-consuming and costly, and novel data collection and analysis methods are needed. In this review paper, we describe the existing algorithms and metrics that use the real-world data to qualify and quantify primary care, including the identification of an individual's likely PCP (identification of plurality provider and major provider), assessment of process quality (for example, appropriate-care-model composite measures), and continuity and regularity of care index (including the interval index, variance index and relative variance index), and highlight the strength and limitation of real world data from electronic health records (EHRs) and claims data in determining the quality of PCP care. The EHR audits facilitate assessing the quality of the workflow process and clinical appropriateness of primary care practices. With extensive and diverse records, administrative claims data can provide reliable information as it assesses primary care quality through coded information from different providers or networks. The use of EHRs and administrative claims data may be a cost-effective analytic strategy for evaluating the quality of primary care.
- Published
- 2023
25. An efficient General Transit Feed Specification (GTFS) enabled algorithm for dynamic transit accessibility analysis.
- Author
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Fayyaz S., S. Kiavash, Liu, Xiaoyue Cathy, and Zhang, Guohui
- Subjects
METROPOLITAN areas ,PUBLIC transit ,TRAVEL time (Traffic engineering) ,POPULATION density ,POPULATION biology - Abstract
The social functions of urbanized areas are highly dependent on and supported by the convenient access to public transportation systems, particularly for the less privileged populations who have restrained auto ownership. To accurately evaluate the public transit accessibility, it is critical to capture the spatiotemporal variation of transit services. This can be achieved by measuring the shortest paths or minimum travel time between origin-destination (OD) pairs at each time-of-day (e.g. every minute). In recent years, General Transit Feed Specification (GTFS) data has been gaining popularity for between-station travel time estimation due to its interoperability in spatiotemporal analytics. Many software packages, such as ArcGIS, have developed toolbox to enable the travel time estimation with GTFS. They perform reasonably well in calculating travel time between OD pairs for a specific time-of-day (e.g. 8:00 AM), yet can become computational inefficient and unpractical with the increase of data dimensions (e.g. all times-of-day and large network). In this paper, we introduce a new algorithm that is computationally elegant and mathematically efficient to address this issue. An open-source toolbox written in C++ is developed to implement the algorithm. We implemented the algorithm on City of St. George’s transit network to showcase the accessibility analysis enabled by the toolbox. The experimental evidence shows significant reduction on computational time. The proposed algorithm and toolbox presented is easily transferable to other transit networks to allow transit agencies and researchers perform high resolution transit performance analysis. [ABSTRACT FROM AUTHOR]
- Published
- 2017
- Full Text
- View/download PDF
26. Windowed persistent homology: A topological signal processing algorithm applied to clinical obesity data.
- Author
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Biwer, Craig, Rothberg, Amy, IglayReger, Heidi, Derksen, Harm, Burant, Charles F., and Najarian, Kayvan
- Subjects
OBESITY ,HOMOLOGY theory ,SIGNAL processing ,ALGORITHMS ,WEIGHT loss ,WEIGHT gain - Abstract
Overweight and obesity are highly prevalent in the population of the United States, affecting roughly 2/3 of Americans. These diseases, along with their associated conditions, are a major burden on the healthcare industry in terms of both dollars spent and effort expended. Volitional weight loss is attempted by many, but weight regain is common. The ability to predict which patients will lose weight and successfully maintain the loss versus those prone to regain weight would help ease this burden by allowing clinicians the ability to skip treatments likely to be ineffective. In this paper we introduce a new windowed approach to the persistent homology signal processing algorithm that, when paired with a modified, semimetric version of the Hausdorff distance, can differentiate the two groups where other commonly used methods fail. The novel approach is tested on accelerometer data gathered from an ongoing study at the University of Michigan. While most standard approaches to signal processing show no difference between the two groups, windowed persistent homology and the modified Hausdorff semimetric show a clear separation. This has significant implications for clinical decision making and patient care. [ABSTRACT FROM AUTHOR]
- Published
- 2017
- Full Text
- View/download PDF
27. Relay discovery and selection for large-scale P2P streaming.
- Author
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Zhang, Chengwei, Wang, Angela Yunxian, and Hei, Xiaojun
- Subjects
PEER-to-peer architecture (Computer networks) ,ERROR analysis in mathematics ,ESTIMATION theory ,HASHING ,NUMERICAL analysis - Abstract
In peer-to-peer networks, application relays have been commonly used to provide various networking services. The service performance often improves significantly if a relay is selected appropriately based on its network location. In this paper, we studied the location-aware relay discovery and selection problem for large-scale P2P streaming networks. In these large-scale and dynamic overlays, it incurs significant communication and computation cost to discover a sufficiently large relay candidate set and further to select one relay with good performance. The network location can be measured directly or indirectly with the tradeoffs between timeliness, overhead and accuracy. Based on a measurement study and the associated error analysis, we demonstrate that indirect measurements, such as King and Internet Coordinate Systems (ICS), can only achieve a coarse estimation of peers’ network location and those methods based on pure indirect measurements cannot lead to a good relay selection. We also demonstrate that there exists significant error amplification of the commonly used “best-out-of-K” selection methodology using three RTT data sets publicly available. We propose a two-phase approach to achieve efficient relay discovery and accurate relay selection. Indirect measurements are used to narrow down a small number of high-quality relay candidates and the final relay selection is refined based on direct probing. This two-phase approach enjoys an efficient implementation using the Distributed-Hash-Table (DHT). When the DHT is constructed, the node keys carry the location information and they are generated scalably using indirect measurements, such as the ICS coordinates. The relay discovery is achieved efficiently utilizing the DHT-based search. We evaluated various aspects of this DHT-based approach, including the DHT indexing procedure, key generation under peer churn and message costs. [ABSTRACT FROM AUTHOR]
- Published
- 2017
- Full Text
- View/download PDF
28. Mapping technological innovation dynamics in artificial intelligence domains: Evidence from a global patent analysis
- Author
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Na Liu, Philip Shapira, Xiaoxu Yue, and Jiancheng Guan
- Subjects
Computer and Information Sciences ,China ,Technology ,Asia ,Science ,Social Sciences ,Technology/methods ,Research and Analysis Methods ,Machine Learning ,Geographical Locations ,Patents as Topic ,Machine Learning Algorithms ,Automation ,Japan ,Inventions ,Artificial Intelligence ,Support Vector Machines ,Humans ,Patents ,Language Acquisition ,Multidisciplinary ,Models, Statistical ,Applied Mathematics ,Simulation and Modeling ,Linguistics ,Automation/methods ,United States ,Intellectual Property ,Models, Organizational ,Physical Sciences ,People and Places ,North America ,Medicine ,Law and Legal Sciences ,Commercial Law ,Diffusion of Innovation ,Mathematics ,Algorithms ,Research Article - Abstract
Artificial intelligence (AI) is emerging as a technology at the center of many political, economic, and societal debates. This paper formulates a new AI patent search strategy and applies this to provide a landscape analysis of AI innovation dynamics and technology evolution. The paper uses patent analyses, network analyses, and source path link count algorithms to examine AI spatial and temporal trends, cooperation features, cross-organization knowledge flow and technological routes. Results indicate a growing yet concentrated, non-collaborative and multi-path development and protection profile for AI patenting, with cross-organization knowledge flows based mainly on interorganizational knowledge citation links.
- Published
- 2021
29. Algorithmes et intelligence artificielle : une note sur l'état de la réglementation des technologies utilisant la reconnaissance faciale automatique au Canada et aux États-Unis.
- Author
-
Nzobonimpa, Stany
- Abstract
Copyright of Governance Review / Revue Gouvernance is the property of University of Ottawa, Center on Governance and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Published
- 2022
- Full Text
- View/download PDF
30. Seed-Fill-Shift-Repair: A redistricting heuristic for civic deliberation
- Author
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Lee Hachadoorian, Christian Haas, Peter Miller, Steven O. Kimbrough, and Frederic H. Murphy
- Subjects
Social Sciences ,Public administration ,Elections ,Geographical locations ,American Community Survey ,Sociology ,Electoral district ,050602 political science & public administration ,Heuristics ,media_common ,050502 law ,education.field_of_study ,Schools ,Multidisciplinary ,Applied Mathematics ,Simulation and Modeling ,05 social sciences ,Politics ,Arizona ,0506 political science ,Redistricting ,Research Design ,Physical Sciences ,Medicine ,Polity ,Algorithms ,Research Article ,Optimization ,Census ,Science ,Political Science ,media_common.quotation_subject ,Population ,Gerrymandering ,Research and Analysis Methods ,Education ,Political science ,Humans ,education ,0505 law ,Survey Research ,Descriptive statistics ,Pennsylvania ,Deliberation ,United States ,North America ,People and places ,Mathematics - Abstract
Political redistricting is the redrawing of electoral district boundaries. It is normally undertaken to reflect population changes. The process can be abused, in what is called gerrymandering, to favor one party or interest group over another, resulting thereby in broadly undemocratic outcomes that misrepresent the views of the voters. Gerrymandering is especially vexing in the United States. This paper introduces an algorithm, with an implementation, for creating districting plans (whether for political redistricting or for other districting applications). The algorithm, Seed-Fill-Shift-Repair (SFSR), is demonstrated for Congressional redistricting in American states. SFSR is able to create thousands of valid redistricting plans, which may then be used as points of departure for public deliberation regarding how best to redistrict a given polity. The main objectives of this paper are: (i) to present SFSR in a broadly accessible form, including code that implements it and test data, so that it may be used for both civic deliberations by the public and for research purposes. (ii) to make the case for what SFSR essays to do, which is to approach redistricting, and districting generally, from a constraint satisfaction perspective and from the perspective of producing a plurality of feasible solutions that may then serve in subsequent deliberations. To further these goals, we make the code publicly available. The paper presents, for illustration purposes, a corpus of 11,206 valid redistricting plans for the Commonwealth of Pennsylvania (produced by SFSR), using the 2017 American Community Survey, along with descriptive statistics. Also, the paper presents 1,000 plans for each of the states of Arizona, Michigan, North Carolina, Pennsylvania, and Wisconsin, using the 2018 American Community Survey, along with descriptive statistics on these plans and the computations involved in their creation.
- Published
- 2020
31. Mapping climate discourse to climate opinion: An approach for augmenting surveys with social media to enhance understandings of climate opinion in the United States
- Author
-
Benjamin Rachunok, Jackson B. Bennett, Roger Flage, and Roshanak Nateghi
- Subjects
Atmospheric Science ,010504 meteorology & atmospheric sciences ,Climate ,Social Sciences ,010501 environmental sciences ,Surveys ,01 natural sciences ,Global Warming ,Machine Learning ,Mathematical and Statistical Techniques ,Sociology ,Surveys and Questionnaires ,Climatology ,education.field_of_study ,Multidisciplinary ,Geography ,Statistics ,Social Communication ,Public relations ,Social Networks ,Research Design ,Physical Sciences ,Medicine ,Natural language ,Algorithms ,Network Analysis ,Research Article ,Computer and Information Sciences ,Process (engineering) ,Science ,Climate Change ,Population ,Twitter ,Climate change ,Research and Analysis Methods ,Artificial Intelligence ,Social media ,Statistical Methods ,education ,0105 earth and related environmental sciences ,Motivation ,Survey Research ,business.industry ,Samfunnsvitenskap: 200 [VDP] ,Global warming ,Models, Theoretical ,United States ,Communications ,Framing (social sciences) ,Attitude ,13. Climate action ,Sustainability ,Earth Sciences ,Anthropogenic Climate Change ,business ,Social Media ,Mathematics ,Forecasting - Abstract
Surveys are commonly used to quantify public opinions of climate change and to inform sustainability policies. However, conducting large-scale population-based surveys is often a difficult task due to time and resource constraints. This paper outlines a machine learning framework—grounded in statistical learning theory and natural language processing—to augment climate change opinion surveys with social media data. The proposed framework maps social media discourse to climate opinion surveys, allowing for discerning the regionally distinct topics and themes that contribute to climate opinions. The analysis reveals significant regional variation in the emergent social media topics associated with climate opinions. Furthermore, significant correlation is identified between social media discourse and climate attitude. However, the dependencies between topic discussion and climate opinion are not always intuitive and often require augmenting the analysis with a topic’s most frequent n-grams and most representative tweets to effectively interpret the relationship. Finally, the paper concludes with a discussion of how these results can be used in the policy framing process to quickly and effectively understand constituents’ opinions on critical issues.
- Published
- 2020
32. Estimation of the shared mobility demand based on the daily regularity of the urban mobility and the similarity of individual trips
- Author
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Veve, Cyril, Chiabaut, Nicolas, Laboratoire d'Ingénierie Circulation Transport (LICIT UMR TE ), and École Nationale des Travaux Publics de l'État (ENTPE)-Université de Lyon-Université Gustave Eiffel
- Subjects
Economics ,Shared mobility ,NEW YORK ,INDIVIDUAL TRIPS ,Social Sciences ,Transportation ,Geographical locations ,Cognition ,Customer base ,11. Sustainability ,TRAFIC ROUTIER ,Psychology ,Economic impact analysis ,ECOMOBILITE ,0303 health sciences ,Multidisciplinary ,Geography ,Applied Mathematics ,Simulation and Modeling ,05 social sciences ,Transportation Infrastructure ,COVOITURAGE ,GESTION DU TRAFIC ,Physical Sciences ,Engineering and Technology ,Medicine ,Algorithms ,Research Article ,Science ,Decision Making ,DEPLACEMENT URBAIN ,Human Geography ,Research and Analysis Methods ,Civil Engineering ,03 medical and health sciences ,MOBILITY DEMAND ,0502 economics and business ,Similarity (psychology) ,Cities ,030304 developmental biology ,Estimation ,MOBILITE ,050210 logistics & transportation ,Models, Statistical ,TRAITEMENT DES DONNEES ,Arithmetic ,Cognitive Psychology ,Biology and Life Sciences ,Environmental economics ,URBAN MOBILITY ,[INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation ,United States ,Economic Analysis ,Roads ,Economic Impact Analysis ,North America ,Earth Sciences ,Human Mobility ,Cognitive Science ,TRIPS architecture ,Business ,VOLUME DE TRAFIC ,People and places ,Mathematics ,Neuroscience - Abstract
Even if shared mobility services are encouraged by transportation policies, they remain underused and inefficient transportation modes because they struggle to find their customer base. This paper aims to estimate the potential demand for such services by focusing on individual trips and determining the number of passengers who perform similar trips. Contrary to existing papers, this study focuses on the demand without assuming any specific shared mobility system. The experiment performed on data coming from New York City conducts to cluster more than 85% of the trips. Consequently, shared mobility services such as ride-sharing can find their customer base and, at a long time, to a significantly reduce the number of cars flowing in the city. After a detailed analysis, commonalities in the clusters are identified: regular patterns from one day to the next exist in shared mobility demand. This regularity makes it possible to anticipate the potential shared mobility demand to help transportation suppliers to optimize their operations.
- Published
- 2020
33. A leader-follower model for discrete competitive facility location problem under the partially proportional rule with a threshold
- Author
-
Wuyang Yu
- Subjects
0209 industrial biotechnology ,Computer science ,0211 other engineering and technologies ,Social Sciences ,02 engineering and technology ,Geographical locations ,020901 industrial engineering & automation ,Cognition ,Mississippi ,Chain (algebraic topology) ,Psychology ,Market share ,Workplace ,021103 operations research ,Multidisciplinary ,Economic Competition ,Applied Mathematics ,Simulation and Modeling ,Commerce ,Facility location problem ,Models, Economic ,Physical Sciences ,Florida ,Medicine ,Leader follower ,Algorithms ,Research Article ,Mathematical optimization ,Current (mathematics) ,Science ,Decision Making ,New York ,Models, Psychological ,Research and Analysis Methods ,Ranking Algorithms ,Humans ,Cognitive Psychology ,Biology and Life Sciences ,Consumer Behavior ,Pennsylvania ,Louisiana ,United States ,Leadership ,Ranking ,North America ,Cognitive Science ,People and places ,Mathematics ,Neuroscience - Abstract
When consumers are faced with the choice of competitive chain facilities that offer exclusive services, current rules do not properly describe the behavior pattern of these consumers. To eliminate the gap between the current rules and this kind of customers behavior pattern, the partially proportional rule with a threshold is proposed in this paper. A leader-follower model for discrete competitive facility location problem is established under the partially proportional rule with a threshold. Combining with the greedy strategy and the 2-opt strategy, a heuristical algorithm (GFA) is designed to solve the follower's problem. By embedding the algorithm (GFA), an improved ranking-based algorithm (IRGA) is proposed to solve the leader-follower model. Numerical tests show that the algorithm proposed in this paper can solve the leader-follower model for discrete competitive facility location problem effectively. The effects of different parameters on the market share captured by the leader firm and the follower firm are analyzed in detail using a quasi-real example. An interesting finding is that in some cases the leader firm does not have a first-mover advantage.
- Published
- 2019
34. A Fast Algorithm to Estimate the Deepest Points of Lakes for Regional Lake Registration.
- Author
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Shen, Zhanfeng, Yu, Xinju, Sheng, Yongwei, Li, Junli, and Luo, Jiancheng
- Subjects
Algorithms ,United States ,Lakes ,General Science & Technology - Abstract
When conducting image registration in the U.S. state of Alaska, it is very difficult to locate satisfactory ground control points because ice, snow, and lakes cover much of the ground. However, GCPs can be located by seeking stable points from the extracted lake data. This paper defines a process to estimate the deepest points of lakes as the most stable ground control points for registration. We estimate the deepest point of a lake by computing the center point of the largest inner circle (LIC) of the polygon representing the lake. An LIC-seeking method based on Voronoi diagrams is proposed, and an algorithm based on medial axis simplification (MAS) is introduced. The proposed design also incorporates parallel data computing. A key issue of selecting a policy for partitioning vector data is carefully studied, the selected policy that equalize the algorithm complexity is proved the most optimized policy for vector parallel processing. Using several experimental applications, we conclude that the presented approach accurately estimates the deepest points in Alaskan lakes; furthermore, we gain perfect efficiency using MAS and a policy of algorithm complexity equalization.
- Published
- 2015
35. Study Results from Indiana University School of Medicine Broaden Understanding of Mathematics (A Flexible Time-varying Coefficient Rate Model for Panel Count Data).
- Subjects
MATHEMATICS ,EXPECTATION-maximization algorithms ,SEXUALLY transmitted diseases - Abstract
A study conducted by researchers at Indiana University School of Medicine has developed a new mathematical model for analyzing recurrent events. The model, called a flexible time-varying coefficient rate model, addresses the limitations of existing rate models by allowing for time-varying covariate effects. The researchers used an efficient algorithm to fit the model and demonstrated its consistency and performance through simulation studies. The model was applied to analyze data from a clinical study on behavioral risk factors for sexually transmitted infections. This research was supported by the NIH National Institute on Alcohol Abuse & Alcoholism. [Extracted from the article]
- Published
- 2024
36. Researchers from Regeneron Pharmaceuticals Inc. Provide Details of New Studies and Findings in the Area of Atopic Dermatitis (Application of Multiple Validated Algorithms for Identifying Incident Breast Cancer Among Individuals With Atopic...).
- Subjects
ATOPIC dermatitis ,BREAST cancer ,RESEARCH personnel ,DRUGS ,ALGORITHMS - Abstract
Researchers from Regeneron Pharmaceuticals Inc. conducted a study on the application of multiple validated algorithms for identifying incident breast cancer among individuals with atopic dermatitis (AD). They tested two algorithms in multiple insurance claims databases and found that Algorithm 2 provided similar estimates to those of the Surveillance, Epidemiology, and End Results (SEER) program. However, they noted that the replicability of these algorithms in different data sources or subpopulations is rarely tested, and caution should be exercised when using them to calculate incidence or prevalence estimates. The study was supported by Sanofi and published in Pharmacoepidemiology and Drug Safety. [Extracted from the article]
- Published
- 2024
37. A personalized channel recommendation and scheduling system considering both section video clips and full video clips
- Author
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SeungGwan Lee and Daeho Lee
- Subjects
Computer science ,Section (typography) ,Video Recording ,Social Sciences ,lcsh:Medicine ,02 engineering and technology ,computer.software_genre ,Geographical locations ,Machine Learning ,Database and Informatics Methods ,Learning and Memory ,0202 electrical engineering, electronic engineering, information engineering ,Data Mining ,Psychology ,Computer Networks ,CLIPS ,lcsh:Science ,Statistical Data ,computer.programming_language ,Multidisciplinary ,Multimedia ,Applied Mathematics ,Simulation and Modeling ,IPTV ,Scheduling system ,Physical Sciences ,Information Retrieval ,020201 artificial intelligence & image processing ,Information Technology ,Algorithms ,Statistics (Mathematics) ,Research Article ,Communication channel ,Computer and Information Sciences ,Schedule ,Minnesota ,Broadcasting ,Research and Analysis Methods ,Computer Communication Networks ,Artificial Intelligence ,Learning ,Humans ,Internet ,business.industry ,Communications Media ,lcsh:R ,Cognitive Psychology ,Biology and Life Sciences ,020207 software engineering ,Models, Theoretical ,United States ,North America ,Cognitive Science ,lcsh:Q ,People and places ,business ,computer ,Mathematics ,Neuroscience - Abstract
With the convergence of various broadcasting systems, the amount of content available in mobile terminals including IPTV has significantly increased. In this paper, we propose a system that enables users to schedule programs considering both section video clips and full video clips based on the user detection method with similar preference. And, since the system constituting the contents can be classified according to the program, the proposed method can store a program desired by the user, and thus create and schedule a kind of individual channel. Experimental results show that the proposed method has a higher prediction accuracy; this is accomplished by comparing existing channel recommendation methods with the program recommendation methods proposed in this paper.
- Published
- 2018
38. Posterior Estimates of Dynamic Constants in HIV Transmission Modeling
- Author
-
Renee Dale, Yingqing Chen, Quoc-Anh T. Le, and Hongyu He
- Subjects
0301 basic medicine ,Risk ,Population level ,Article Subject ,Computer science ,Computation ,Population ,Population Dynamics ,HIV Infections ,lcsh:Computer applications to medicine. Medical informatics ,Global Health ,General Biochemistry, Genetics and Molecular Biology ,03 medical and health sciences ,Statistics ,Applied mathematics ,Quantitative Biology::Populations and Evolution ,Humans ,Computer Simulation ,Least-Squares Analysis ,MATLAB ,Hiv transmission ,education ,computer.programming_language ,education.field_of_study ,General Immunology and Microbiology ,Applied Mathematics ,Linear model ,General Medicine ,030112 virology ,United States ,Nonlinear system ,Discrete time and continuous time ,Nonlinear Dynamics ,Modeling and Simulation ,Linear Models ,lcsh:R858-859.7 ,computer ,Algorithms ,Software ,Research Article - Abstract
In this paper, we construct a linear differential system in both continuous time and discrete time to model HIV transmission on the population level. The main question is the determination of parameters based on the posterior information obtained from statistical analysis of the HIV population. We call these parameters dynamic constants in the sense that these constants determine the behavior of the system in various models. There is a long history of using linear or nonlinear dynamic systems to study the HIV population dynamics or other infectious diseases. Nevertheless, the question of determining the dynamic constants in the system has not received much attention. In this paper, we take some initial steps to bridge such a gap. We study the dynamic constants that appear in the linear differential system model in both continuous and discrete time. Our computations are mostly carried out in Matlab.
- Published
- 2017
39. BMC Infectious Diseases
- Author
-
Farzaneh Sadat Tabataba, Naren Ramakrishnan, Srinivasan Venkatramanan, Bryan Lewis, Jiangzhuo Chen, Madhav V. Marathe, Prithwish Chakraborty, and Computer Science
- Subjects
FOS: Computer and information sciences ,0301 basic medicine ,Physics - Physics and Society ,Error Measure ,Computer science ,FOS: Physical sciences ,Future trend ,Physics and Society (physics.soc-ph) ,Multiple methods ,Machine learning ,computer.software_genre ,lcsh:Infectious and parasitic diseases ,Disease Outbreaks ,03 medical and health sciences ,0302 clinical medicine ,Schema (psychology) ,Influenza, Human ,Humans ,lcsh:RC109-216 ,030212 general & internal medicine ,Computational epidemiology ,Quantitative Biology - Populations and Evolution ,Pandemics ,Social and Information Networks (cs.SI) ,Stochastic Processes ,business.industry ,Populations and Evolution (q-bio.PE) ,Probabilistic logic ,Age Factors ,Computer Science - Social and Information Networks ,Models, Theoretical ,United States ,3. Good health ,Epidemic forecasting ,030104 developmental biology ,Infectious Diseases ,Technical Advance ,FOS: Biological sciences ,Performance evaluation ,Artificial intelligence ,Ranking ,Epidemic-Features ,business ,computer ,Algorithms ,Forecasting - Abstract
Background: Over the past few decades, numerous forecasting methods have been proposed in the field of epidemic forecasting. Such methods can be classified into different categories such as deterministic vs. probabilistic, comparative methods vs. generative methods, and so on. In some of the more popular comparative methods, researchers compare observed epidemiological data from early stages of an outbreak with the output of proposed models to forecast the future trend and prevalence of the pandemic. A significant problem in this area is the lack of standard well-defined evaluation measures to select the best algorithm among different ones, as well as for selecting the best possible configuration for a particular algorithm. Results: In this paper, we present an evaluation framework which allows for combining different features, error measures, and ranking schema to evaluate forecasts. We describe the various epidemic features (Epi-features) included to characterize the output of forecasting methods and provide suitable error measures that could be used to evaluate the accuracy of the methods with respect to these Epi-features. We focus on long-term predictions rather than short-term forecasting and demonstrate the utility of the framework by evaluating six forecasting methods for predicting influenza in the United States. Our results demonstrate that different error measures lead to different rankings even for a single Epi-feature. Further, our experimental analyses show that no single method dominates the rest in predicting all Epi-features, when evaluated across error measures. As an alternative, we provide various consensus ranking schema that summarizes individual rankings, thus accounting for different error measures. We believe that a comprehensive evaluation framework, as presented in this paper, will add value to the computational epidemiology community., Submitted to BMC infectious disease Journal, 2016. Accepted in 2017
- Published
- 2017
40. MOVES-Matrix and distributed computing for microscale line source dispersion analysis
- Author
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Michael O Rodgers, Yanzhi Ann Xu, Xiaodan Xu, Randall Guensler, and Haobing Liu
- Subjects
Engineering ,Georgia ,010504 meteorology & atmospheric sciences ,Distributed computing ,Transportation ,Management, Monitoring, Policy and Law ,01 natural sciences ,Line source ,0502 economics and business ,Extensive data ,United States Environmental Protection Agency ,Waste Management and Disposal ,Microscale chemistry ,AERMOD ,0105 earth and related environmental sciences ,computer.programming_language ,Vehicle Emissions ,050210 logistics & transportation ,Air Pollutants ,business.industry ,05 social sciences ,Atmospheric dispersion modeling ,Python (programming language) ,Models, Theoretical ,United States ,business ,computer ,Algorithms ,Environmental Monitoring - Abstract
MOVES and AERMOD are the U.S. Environmental Protection Agency's recommended models for use in project-level transportation conformity and hot-spot analysis. However, the structure and algorithms involved in running MOVES make analyses cumbersome and time-consuming. Likewise, the modeling setup process, including extensive data requirements and required input formats, in AERMOD lead to a high potential for analysis error in dispersion modeling. This study presents a distributed computing method for line source dispersion modeling that integrates MOVES-Matrix, a high-performance emission modeling tool, with the microscale dispersion models CALINE4 and AERMOD. MOVES-Matrix was prepared by iteratively running MOVES across all possible iterations of vehicle source-type, fuel, operating conditions, and environmental parameters to create a huge multi-dimensional emission rate lookup matrix. AERMOD and CALINE4 are connected with MOVES-Matrix in a distributed computing cluster using a series of Python scripts. This streamlined system built on MOVES-Matrix generates exactly the same emission rates and concentration results as using MOVES with AERMOD and CALINE4, but the approach is more than 200 times faster than using the MOVES graphical user interface. Because AERMOD requires detailed meteorological input, which is difficult to obtain, this study also recommends using CALINE4 as a screening tool for identifying the potential area that may exceed air quality standards before using AERMOD (and identifying areas that are exceedingly unlikely to exceed air quality standards). CALINE4 worst case method yields consistently higher concentration results than AERMOD for all comparisons in this paper, as expected given the nature of the meteorological data employed.The paper demonstrates a distributed computing method for line source dispersion modeling that integrates MOVES-Matrix with the CALINE4 and AERMOD. This streamlined system generates exactly the same emission rates and concentration results as traditional way to use MOVES with AERMOD and CALINE4, which are regulatory models approved by the U.S. EPA for conformity analysis, but the approach is more than 200 times faster than implementing the MOVES model. We highlighted the potentially significant benefit of using CALINE4 as screening tool for identifying potential area that may exceeds air quality standards before using AERMOD, which requires much more meteorology input than CALINE4.
- Published
- 2017
41. Applying GIS and Machine Learning Methods to Twitter Data for Multiscale Surveillance of Influenza
- Author
-
Anoshé A Aslam, Anna C Nagel, Ming-Hsiang Tsou, Chris Allen, and Jean Mark Gawron
- Subjects
Viral Diseases ,Geographic information system ,020205 medical informatics ,Computer science ,lcsh:Medicine ,Social Sciences ,02 engineering and technology ,Filter (software) ,Disease Outbreaks ,Machine Learning ,0302 clinical medicine ,Public health surveillance ,Sociology ,Geoinformatics ,0202 electrical engineering, electronic engineering, information engineering ,Information system ,Medicine and Health Sciences ,Public and Occupational Health ,Public Health Surveillance ,030212 general & internal medicine ,Geography, Medical ,lcsh:Science ,Multidisciplinary ,Geography ,Applied Mathematics ,Simulation and Modeling ,Social Communication ,3. Good health ,Infectious Diseases ,Social Networks ,Physical Sciences ,Network Analysis ,Algorithms ,Research Article ,Computer and Information Sciences ,Twitter ,Research and Analysis Methods ,03 medical and health sciences ,Machine Learning Algorithms ,Artificial Intelligence ,Support Vector Machines ,Influenza, Human ,Flu season ,Humans ,Social media ,business.industry ,lcsh:R ,Outbreak ,Biology and Life Sciences ,Data science ,Communications ,Influenza ,United States ,Geographic Information Systems ,Earth Sciences ,Cognitive Science ,lcsh:Q ,business ,Social Media ,Mathematics ,Neuroscience - Abstract
Traditional methods for monitoring influenza are haphazard and lack fine-grained details regarding the spatial and temporal dynamics of outbreaks. Twitter gives researchers and public health officials an opportunity to examine the spread of influenza in real-time and at multiple geographical scales. In this paper, we introduce an improved framework for monitoring influenza outbreaks using the social media platform Twitter. Relying upon techniques from geographic information science (GIS) and data mining, Twitter messages were collected, filtered, and analyzed for the thirty most populated cities in the United States during the 2013-2014 flu season. The results of this procedure are compared with national, regional, and local flu outbreak reports, revealing a statistically significant correlation between the two data sources. The main contribution of this paper is to introduce a comprehensive data mining process that enhances previous attempts to accurately identify tweets related to influenza. Additionally, geographical information systems allow us to target, filter, and normalize Twitter messages.
- Published
- 2016
42. Challenges and Insights in Using HIPAA Privacy Rule for Clinical Text Annotation
- Author
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Mehmet, Kayaalp, Allen C, Browne, Pamela, Sagan, Tyne, McGee, and Clement J, McDonald
- Subjects
Health Insurance Portability and Accountability Act ,Privacy ,Data Anonymization ,Electronic Health Records ,Humans ,Articles ,Personally Identifiable Information ,Algorithms ,Confidentiality ,United States - Abstract
The Privacy Rule of Health Insurance Portability and Accountability Act (HIPAA) requires that clinical documents be stripped of personally identifying information before they can be released to researchers and others. We have been manually annotating clinical text since 2008 in order to test and evaluate an algorithmic clinical text de-identification tool, NLM Scrubber, which we have been developing in parallel. Although HIPAA provides some guidance about what must be de-identified, translating those guidelines into practice is not as straightforward, especially when one deals with free text. As a result we have changed our manual annotation labels and methods six times. This paper explains why we have made those annotation choices, which have been evolved throughout seven years of practice on this field. The aim of this paper is to start a community discussion towards developing standards for clinical text annotation with the end goal of studying and comparing clinical text de-identification systems more accurately.
- Published
- 2016
43. Comparative Study of Physical Education Teaching in Middle Schools at Home and Abroad Using Clustering Algorithm
- Author
-
Dejun Tan
- Subjects
China ,Physical Education and Training ,Schools ,Article Subject ,Adolescent ,General Computer Science ,Teaching ,General Mathematics ,General Neuroscience ,General Medicine ,United States ,Cluster Analysis ,Humans ,Algorithms - Abstract
Physical education in middle school is very important for teenagers, so it is also crucial to understand the differences between PET (Physical Education Teaching) systems in middle schools at home and abroad. The frontier and hotspot of PET research in middle schools at home and abroad are examined in this paper using citation analysis, information visualization, and cluster analysis, as well as CiteSpace software. The findings show that PET method research in China is qualitative, whereas PET method research in middle schools around the world is quantitative evaluation and empirical research. Domestic research hotspots focus on classroom instructional design, whereas foreign countries focus on load identification theory’s application in instructional design. Frontier research in the United States is dispersed and covers a wide range of topics, whereas research in other countries focuses on cognitive load theory. The classification time of this improved algorithm is reduced by 190.97 seconds when compared to the traditional KNN algorithm, and the total time is increased by more than 50%. According to the findings, nonsports or nonsports influencing factors should be given more consideration in the study of adolescent physical fitness decline in China.
- Published
- 2022
44. Dynamics in the Fitness-Income plane: Brazilian states vs World countries
- Author
-
Luciano Pietronero, Andrea Gabrielli, Emanuele Pugliese, José S. Andrade, Felipe G. Operti, Operti, F. G., Pugliese, E., Andrade, J. S., Pietronero, L., and Gabrielli, A.
- Subjects
Economic Complexity ,Complex Systems ,Dinamical Systems ,010504 meteorology & atmospheric sciences ,Economics ,lcsh:Medicine ,Social Sciences ,01 natural sciences ,Geographical locations ,Gross domestic product ,Russia ,Spectrum Analysis Techniques ,Mathematical and Statistical Techniques ,Econometrics ,Per capita ,050207 economics ,lcsh:Science ,Multidisciplinary ,Applied Mathematics ,Simulation and Modeling ,05 social sciences ,Absorption Spectroscopy ,BRIC ,Europe ,Physical Sciences ,Metric (mathematics) ,Income ,Algorithms ,Statistics (Mathematics) ,Brazil ,Human ,Research Article ,Asia ,Gross Domestic Product ,India ,Developing country ,Research and Analysis Methods ,Developing Countrie ,Life Expectancy ,0502 economics and business ,Humans ,Statistical Methods ,Predictability ,Developing Countries ,0105 earth and related environmental sciences ,lcsh:R ,Correction ,South America ,United States ,Socioeconomic Factors ,Ranking ,Economic complexity index ,North America ,People and Places ,lcsh:Q ,Mathematics ,Forecasting - Abstract
In this paper we introduce a novel algorithm, called Exogenous Fitness, to calculate the Fitness of subnational entities and we apply it to the states of Brazil. In the last decade, several indices were introduced to measure the competitiveness of countries by looking at the complexity of their export basket. Tacchella et al (2012) developed a non-monetary metric called Fitness. In this paper, after an overview about Brazil as a whole and the comparison with the other BRIC countries, we introduce a new methodology based on the Fitness algorithm, called Exogenous Fitness. Combining the results with the Gross Domestic Product per capita (GDP(p)), we look at the dynamics of the Brazilian states in the Fitness-Income plane. Two regimes are distinguishable: one with high predictability and the other with low predictability, showing a deep analogy with the heterogeneous dynamics of the World countries. Furthermore, we compare the ranking of the Brazilian states according to the Exogenous Fitness with the ranking obtained through two other techniques, namely Endogenous Fitness and Economic Complexity Index.
- Published
- 2018
45. Global and country-specific mainstreaminess measures: Definitions, analysis, and usage for improving personalized music recommendation systems.
- Author
-
Bauer, Christine and Schedl, Markus
- Abstract
Relevance: Popularity-based approaches are widely adopted in music recommendation systems, both in industry and research. These approaches recommend to the target user what is currently popular among all users of the system. However, as the popularity distribution of music items typically is a long-tail distribution, popularity-based approaches to music recommendation fall short in satisfying listeners that have specialized music preferences far away from the global music mainstream. Addressing this gap, the contribution of this article is three-fold. Definition of mainstreaminess measures: First, we provide several quantitative measures describing the proximity of a user’s music preference to the music mainstream. Assuming that there is a difference between the global music mainstream and a country-specific one, we define the measures at two levels: relating a listener’s music preferences to the global music preferences of all users, or relating them to music preferences of the user’s country. To quantify such music preferences, we define a music item’s popularity in terms of artist playcounts (APC) and artist listener counts (ALC). Moreover, we adopt a distribution-based and a rank-based approach as means to decrease bias towards the head of the long-tail distribution. This eventually results in a framework of 6 measures to quantify music mainstream. Differences between countries with respect to music mainstream: Second, we perform in-depth quantitative and qualitative studies of music mainstream in that we (i) analyze differences between countries in terms of their level of mainstreaminess, (ii) uncover both positive and negative outliers (substantially higher and lower country-specific popularity, respectively, compared to the global mainstream), analyzing these with a mixed-methods approach, and (iii) investigate differences between countries in terms of listening preferences related to popular music artists. We conduct our studies and experiments using the standardized LFM-1b dataset, from which we analyze about 800,000,000 listening events shared by about 53,000 users (from 47 countries) of the music streaming platform Last.fm. We show that there are substantial country-specific differences in listeners’ music consumption behavior with respect to the most popular artists listened to. Rating prediction experiments: Third, we demonstrate the applicability of our study results to improve music recommendation systems. To this end, we conduct rating prediction experiments in which we tailor recommendations to a user’s level of preference for the music mainstream using the proposed 6 mainstreaminess measures: defined by a distribution-based or rank-based approach, defined on a global level or on a country level (for the user’s country), and for APC or ALC. Our approach roughly equals a hybrid recommendation approach in which a demographic filtering strategy is implemented before collaborative filtering is performed. Results suggest that, in terms of rating prediction accuracy, each of the presented mainstreaminess definitions has its merits. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
46. A bibliometric of publication trends in medical image segmentation: Quantitative and qualitative analysis
- Author
-
Guangnan Zhang, Bin Zhang, Nader Ale Ebrahim, Bahbibi Rahmatullah, Shir Li Wang, and Huan Wang
- Subjects
History ,Polymers and Plastics ,Computer science ,Scopus ,Industrial and Manufacturing Engineering ,Field (computer science) ,Radiation Oncology Physics ,Radiology, Nuclear Medicine and imaging ,Segmentation ,publication trends ,Business and International Management ,image segmentation ,Instrumentation ,bibliometric ,Radiation ,Information retrieval ,Point (typography) ,business.industry ,Deep learning ,Publications ,Image segmentation ,research productivity ,United States ,medical image ,Quantitative analysis (finance) ,Bibliometrics ,Artificial intelligence ,Citation ,business ,Delivery of Health Care ,Algorithms - Abstract
Purpose: Medical images are important in diagnosing disease and treatment planning. Computer algorithms that describe anatomical structures that highlight regions of interest and remove unnecessary information are collectively known as medical image segmentation algorithms. The quality of these algorithms will directly affect the performance of the following processing steps. There are many studies about the algorithms of medical image segmentation and their applications, but none involved a bibliometric of medical image segmentation. Methods: This bibliometric work investigated the academic publication trends in medical image segmentation technology. These data were collected from the Web of Science (WoS) Core Collection and the Scopus. In the quantitative analysis stage, important visual maps were produced to show publication trends from five different perspectives including annual publications, countries, top authors, publication sources, and keywords. In the qualitative analysis stage, the frequently used methods and research trends in the medical image segmentation field were analyzed from 49 publications with the top annual citation rates. Results: The analysis results showed that the number of publications had increased rapidly by year. The top related countries include the Chinese mainland, the United States, and India. Most of these publications were conference papers, besides there are also some top journals. The research hotspot in this field was deep learning-based medical image segmentation algorithms based on keyword analysis. These publications were divided into three categories: reviews, segmentation algorithm publications, and other relevant publications. Among these three categories, segmentation algorithm publications occupied the vast majority, and deep learning neural network-based algorithm was the research hotspots and frontiers. Conclusions: Through this bibliometric research work, the research hotspot in the medical image segmentation field is uncovered and can point to future research in the field. It can be expected that more researchers will focus their work on deep learning neural network-based medical image segmentation.
- Published
- 2021
47. Evaluating the influential priority of the factors on insurance loss of public transit.
- Author
-
Zhang, Wenhui, Su, Yongmin, Ke, Ruimin, and Chen, Xinqiang
- Subjects
PUBLIC transit ,INSURANCE claims ,GREY relational analysis ,K-means clustering - Abstract
Understanding correlation between influential factors and insurance losses is beneficial for insurers to accurately price and modify the bonus-malus system. Although there have been a certain number of achievements in insurance losses and claims modeling, limited efforts focus on exploring the relative role of accidents characteristics in insurance losses. The primary objective of this study is to evaluate the influential priority of transit accidents attributes, such as the time, location and type of accidents. Based on the dataset from Washington State Transit Insurance Pool (WSTIP) in USA, we implement several key algorithms to achieve the objectives. First, K-means algorithm contributes to cluster the insurance loss data into 6 intervals; second, Grey Relational Analysis (GCA) model is applied to calculate grey relational grades of the influential factors in each interval; in addition, we implement Naive Bayes model to compute the posterior probability of factors values falling in each interval. The results show that the time, location and type of accidents significantly influence the insurance loss in the first five intervals, but their grey relational grades show no significantly difference. In the last interval which represents the highest insurance loss, the grey relational grade of the time is significant higher than that of the location and type of accidents. For each value of the time and location, the insurance loss most likely falls in the first and second intervals which refers to the lower loss. However, for accidents between buses and non-motorized road users, the probability of insurance loss falling in the interval 6 tends to be highest. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
48. Findings from Duke University Broaden Understanding of Ebola Virus (Deep Spatial Q-learning for Infectious Disease Control).
- Subjects
EBOLA virus ,COMMUNICABLE diseases ,PREVENTIVE medicine ,EBOLA virus disease ,VIRUS diseases - Published
- 2023
49. Integration and Optimization of British and American Literature Information Resources in the Distributed Cloud Computing Environment
- Author
-
Mei Chen
- Subjects
Article Subject ,General Computer Science ,General Mathematics ,General Neuroscience ,Publications ,General Medicine ,Cloud Computing ,Models, Theoretical ,Algorithms ,United States - Abstract
One of the most effective approaches to improve resource usage efficiency and degree of resource collecting is to integrate resources. Many studies on the integration of information resources are also available. The search engines are the most well-known. At the same time, this article intends to optimize the integration of British and American literature information resources by employing distributed cloud computing, based on the needs of British and American literature. This research develops a model for the dispersed nature of cloud computing. It optimizes the method by fitting the mathematical model of transmission cost and latency. This article analyzes the weaknesses of the current British and American literature information resource integration and optimizes them for the integration of British and American literature resources. The Random algorithm has the longest delay, according to the results of this paper’s experiments (maximum user weighted distance). The algorithms NPA-PDP and BWF have longer delays than the algorithm Opt. The percentage decline varies between 0.17 percent and 1.11 percent for different algorithms. It demonstrates that the algorithm presented in this work can be used to integrate and maximize information resources from English and American literature.
- Published
- 2022
50. Forty-Eight Week Outcomes of a Site-Randomized Trial of Combined Cognitive Behavioral Therapy and Medication Management Algorithm for Treatment of Depression Among Youth With HIV in the United States
- Author
-
Larry K, Brown, Kristin, Baltrusaitis, Betsy D, Kennard, Graham J, Emslie, Miriam, Chernoff, Sarah, Buisson, Kathryn, Lypen, Laura B, Whiteley, Shirley, Traite, Chelsea, Krotje, Kevin, Knowles, Ellen, Townley, Jaime, Deville, Megan, Wilkins, Dan, Reirden, Mary, Paul, Christy, Beneri, and David E, Shapiro
- Subjects
Depressive Disorder, Major ,Treatment Outcome ,Adolescent ,Cognitive Behavioral Therapy ,Depression ,Medication Therapy Management ,Humans ,HIV Infections ,Child ,Algorithms ,United States - Abstract
Studies suggest that manualized, measurement-guided, depression treatment is more efficacious than usual care but impact can wane. Our study among youth with HIV (YWH), aged 12-24 years at US clinical research sites in the International Maternal Pediatric Adolescent AIDS Clinical Trials Network, found a significant reduction in depressive symptoms among YWH who received a manualized, measurement-guided treatment. This paper reports outcomes up to 24 weeks after the intervention.Eligibility included diagnosis of ongoing nonpsychotic depression. Using restricted randomization, sites were assigned to either combination cognitive behavioral therapy and medication management algorithm tailored for YWH or to enhanced standard of care, which provided psychotherapy and medication management. Site-level mean Quick Inventory for Depression Symptomatology Self-Report (QIDS-SR) scores and proportion of youth with treatment response (gt;50% decrease from baseline) and remission (QIDS-SR ≤ 5) were compared across arms using t tests.Thirteen sites enrolled 156 YWH, with baseline demographic factors, depression severity, and HIV disease status comparable across arms. At week 36, the site-level mean proportions of youth with a treatment response and remission were greater at combination cognitive behavioral therapy and medication management algorithm sites (52.0% vs. 18.8%, P = 0.02; 37.9% vs. 19.4%, P = 0.05), and the mean QIDS-SR was lower (7.45 vs. 9.75, P = 0.05). At week 48, the site-level mean proportion with a treatment response remained significantly greater (58.7% vs. 33.4%, P = 0.047).The impact of manualized, measurement-guided cognitive behavioral therapy and medication management algorithm tailored for YWH that was efficacious at week 24 continued to be evident at weeks 36 and 48.
- Published
- 2022
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