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204. Application of Machine Learning and Grocery Transaction Data to Forecast Effectiveness of Beverage Taxation.

Author

Xing Han Lu, Hiroshi Mamiya, Vybihal, Joseph, Yu Ma, and Buckeridge, David L.

Subjects
MACHINE learning, SOFT drinks, CHRONIC diseases, REGRESSION analysis, ARTIFICIAL neural networks
Abstract

Sugar Sweetened Beverages (SSB) are the primary source of artificially added sugar and have a casual association with chronic diseases. Taxation of SSB has been proposed, but limited evidence exists to guide this public health policy. Grocery transaction data, with price, discounting and other information for beverage products, present an opportunity to evaluate the likely effects of taxation policy. Sales are often non-linearly associated with price and are affected by the prices of multiple competing brands. We evaluated the predictive performance of Boosted Decision Tree Regression (B-DTR) and Deep Neural Networks (DNN) that account for the non-linearity and competition across brands, and compared their performance to a benchmark regression, the Least Absolute Shrinkage and Selection Operator (LASSO). BDTR and DNN showed a lower Mean Squared Error (MSE) of prediction in the sales of most major SSB brands in comparison to LASSO, indicating a superior accuracy in predicting the effectiveness of SSB taxation. We demonstrated the application of machine learning methods and large transactional data from grocery stores to forecast the effectiveness food taxation. [ABSTRACT FROM AUTHOR]

Published
2019
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207. Broadening the perspective of AI in public-health surveillance: from local detection to global epidemic monitoring and control

Author

Buckeridge, David L., Izadi, Masoumeh, Verma, Aman, and Okhmatovskaia, Anya

Subjects
Artificial intelligence -- Usage, Epidemics -- Control, Epidemics -- Diagnosis, Epidemics -- Prevention, Medicine, Preventive -- Technology application, Preventive health services -- Technology application, Artificial intelligence, Technology application, Business, Computers, Computers and office automation industries, Electronics
Published
2009

208. Ontology-centered syndromic surveillance for bioterrorism

Author

Crubezy, Monica, O'Connor, Martin, Buckeridge, David L., Pincus, Zachary, and Musen, Mark A.

Subjects
Bioterrorism -- Analysis, Ontology -- Research, Business, Computers, Computers and office automation industries, Electronics
Abstract

BioStorm (the Biological Spatio-Temporal Outbreak Reasoning Module), an experimental system providing an end-to-end solution to syndromic surveillance, by implementing ontologies, is discussed.

Published
2005

210. Improving patient safety and efficiency of medication reconciliation through the development and adoption of a computer-assisted tool with automated electronic integration of population-based community drug data: the RightRx project

Author

Tamblyn, Robyn, primary, Winslade, Nancy, additional, Lee, Todd C, additional, Motulsky, Aude, additional, Meguerditchian, Ari, additional, Bustillo, Melissa, additional, Elsayed, Sarah, additional, Buckeridge, David L, additional, Couture, Isabelle, additional, Qian, Christina J, additional, Moraga, Teresa, additional, and Huang, Allen, additional

Published
2017
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214. Accuracy and generalizability of using automated methods for identifying adverse events from electronic health record data: a validation study protocol

Author

Rochefort, Christian M., primary, Buckeridge, David L., additional, Tanguay, Andréanne, additional, Biron, Alain, additional, D’Aragon, Frédérick, additional, Wang, Shengrui, additional, Gallix, Benoit, additional, Valiquette, Louis, additional, Audet, Li-Anne, additional, Lee, Todd C., additional, Jayaraman, Dev, additional, Petrucci, Bruno, additional, and Lefebvre, Patricia, additional

Published
2017
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225. Reports on the 2015 AAAI Workshop Series

Author

Albrecht, Stefano V., Beck, J. Christopher, Buckeridge, David L., Botea, Adi, Caragea, Cornelia, Chi, Chi-hung, Damoulas, Theodoros, Dilkina, Bistra, Eaton, Eric, Fazli, Pooyan, Ganzfried, Sam, Giles, C. Lee, Guillet, Sebastien, Holte, Robert, Hutter, Frank, Koch, Thorsten, Leonetti, Matteo, Lindauer, Marius, Machado, Marlos C., Malitsky, Yuri, Marcus, Gary, Meijer, Sebastiaan, Rossi, Francesca, Shaban-Nejad, Arash, Thiebaux, Sylvie, Veloso, Manuela, Walsh, Toby, Wang, Can, Zhang, Jie, Zheng, Yu, Albrecht, Stefano V., Beck, J. Christopher, Buckeridge, David L., Botea, Adi, Caragea, Cornelia, Chi, Chi-hung, Damoulas, Theodoros, Dilkina, Bistra, Eaton, Eric, Fazli, Pooyan, Ganzfried, Sam, Giles, C. Lee, Guillet, Sebastien, Holte, Robert, Hutter, Frank, Koch, Thorsten, Leonetti, Matteo, Lindauer, Marius, Machado, Marlos C., Malitsky, Yuri, Marcus, Gary, Meijer, Sebastiaan, Rossi, Francesca, Shaban-Nejad, Arash, Thiebaux, Sylvie, Veloso, Manuela, Walsh, Toby, Wang, Can, Zhang, Jie, and Zheng, Yu

Abstract

AAAI's 2015 Workshop Program was held Sunday and Monday, January 25-26, 2015, at the Hyatt Regency Austin Hotel in Austin, Texas, USA. The AAAI-15 workshop program included 16 workshops covering a wide range of topics in artificial intelligence. Most workshops were held on a single day. The titles of the workshops included Algorithm Configuration; Artificial Intelligence and Ethics; Artificial Intelligence Applied to Assistive Technologies and Smart Environments; Artificial Intelligence for Cities; Artificial Intelligence for Transportation: Advice, Inter-activity, and Actor Modeling; Beyond the Turing Test; Computational Sustainability; Computer Poker and Imperfect Information; Incentive and Trust in E-Communities; Knowledge, Skill, and Behavior Transfer in Autonomous Robots; Learning for General Competency in Video Games; Multiagent Interaction without Prior Coordination; Planning, Search, and Optimization; Scholarly Big Data: AI Perspectives, Challenges, and Ideas; Trajectory-Based Behaviour Analytics; and World Wide Web and Public Health Intelligence., QC 20150728

Published
2015
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226. Forecasting malaria in a highly endemic country using environmental and clinical predictors.

Author

Zinszer, Kate, Zinszer, Kate, Kigozi, Ruth, Charland, Katia, Dorsey, Grant, Brewer, Timothy F, Brownstein, John S, Kamya, Moses R, Buckeridge, David L, Zinszer, Kate, Zinszer, Kate, Kigozi, Ruth, Charland, Katia, Dorsey, Grant, Brewer, Timothy F, Brownstein, John S, Kamya, Moses R, and Buckeridge, David L

Abstract

BackgroundMalaria thrives in poor tropical and subtropical countries where local resources are limited. Accurate disease forecasts can provide public and clinical health services with the information needed to implement targeted approaches for malaria control that make effective use of limited resources. The objective of this study was to determine the relevance of environmental and clinical predictors of malaria across different settings in Uganda.MethodsForecasting models were based on health facility data collected by the Uganda Malaria Surveillance Project and satellite-derived rainfall, temperature, and vegetation estimates from 2006 to 2013. Facility-specific forecasting models of confirmed malaria were developed using multivariate autoregressive integrated moving average models and produced weekly forecast horizons over a 52-week forecasting period.ResultsThe model with the most accurate forecasts varied by site and by forecast horizon. Clinical predictors were retained in the models with the highest predictive power for all facility sites. The average error over the 52 forecasting horizons ranged from 26 to 128% whereas the cumulative burden forecast error ranged from 2 to 22%.ConclusionsClinical data, such as drug treatment, could be used to improve the accuracy of malaria predictions in endemic settings when coupled with environmental predictors. Further exploration of malaria forecasting is necessary to improve its accuracy and value in practice, including examining other environmental and intervention predictors, including insecticide-treated nets.

Published
2015

227. Characteristics of US public schools with reported cases of novel influenza A (H1N1)

Author

Harvard University--MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology. Media Laboratory, Hoen, Anne Gatewood, Chan, Emily H., Freifeld, Clark C., Keller, Mikaela, Charland, Katia, Brownstein, John S., Buckeridge, David L., Donnelly, Christl A., Harvard University--MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology. Media Laboratory, Hoen, Anne Gatewood, Chan, Emily H., Freifeld, Clark C., Keller, Mikaela, Charland, Katia, Brownstein, John S., Buckeridge, David L., and Donnelly, Christl A.

Abstract

Objective The 2009 pandemic of influenza A (H1N1) has disproportionately affected children and young adults, resulting in attention by public health officials and the news media on schools as important settings for disease transmission and spread. We aimed to characterize US schools affected by novel influenza A (H1N1) relative to other schools in the same communities. Methods A database of US school-related cases was obtained by electronic news media monitoring for early reports of novel H1N1 influenza between April 23 and June 8, 2009. We performed a matched case–control study of 32 public primary and secondary schools that had one or more confirmed cases of H1N1 influenza and 6815 control schools located in the same 23 counties as case schools. Results Compared with controls from the same county, schools with reports of confirmed cases of H1N1 influenza were less likely to have a high proportion of economically disadvantaged students (adjusted odds ratio (aOR) 0.385; 95% confidence interval (CI) 0.166–0.894) and less likely to have older students (aOR 0.792; 95% CI 0.670–0.938). Conclusions We conclude that public schools with younger, more affluent students may be considered sentinels of the epidemic and may have played a role in its initial spread., National Institute of Allergy and Infectious Diseases (U.S.) (R21AI073591-01), National Institutes of Health (U.S.), Canadian Institutes of Health Research (PAN-83152), Canadian Institutes of Health Research (CAT-86857), Google (Firm) (Research Grant)

Published
2015

228. Estimating prevalence using indirect information and Bayesian evidence synthesis.

Author

Luo, Yu, Stephens, David A., and Buckeridge, David L.

Subjects
BAYESIAN analysis, YOUTH with attention-deficit hyperactivity disorder, TREATMENT of attention-deficit hyperactivity disorder, DISEASE prevalence, PUBLIC health
Abstract

Copyright of Canadian Journal of Statistics is the property of Wiley-Blackwell 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
2018
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229. Accuracy of Administrative Health Data for Surveillance of Traumatic Brain Injury: A Bayesian Latent Class Analysis.

Author

Lasry, Oliver, Dendukuri, Nandini, Marcoux, Judith, and Buckeridge, David L.

Abstract

Background: Traumatic brain injury surveillance provides information for allocating resources to prevention efforts. Administrative data are widely available and inexpensive but may underestimate traumatic brain injury burden by misclassifying cases. Moreover, previous studies evaluating the accuracy of administrative data surveillance case definitions were at risk of bias by using imperfect diagnostic definitions as reference standards. We assessed the accuracy (sensitivity/specificity) of traumatic brain injury surveillance case definitions in administrative data, without using a reference standard, to estimate incidence accurately.Methods: We used administrative data from a 25% random sample of Montreal residents from 2000 to 2014. We used hierarchical Bayesian latent class models to estimate the accuracy of widely used traumatic brain injury case definitions based on the International Classification of Diseases, or on head radiologic examinations, covering the full injury spectrum in children, adults, and the elderly. We estimated measurement error-adjusted age- and severity-specific incidence.Results: The adjusted traumatic brain injury incidence was 76 (95% CrI = 68, 85) per 10,000 person-years (underestimated as 54 [95% CrI = 54, 55] per 10,000 without adjustment). The most sensitive case definitions were radiologic examination claims in adults/elderly (0.48; 95% CrI = 0.43, 0.55 and 0.66; 95% CrI = 0.54, 0.79) and emergency department claims in children (0.45; 95% CrI = 0.39, 0.52). The most specific case definitions were inpatient claims and discharge abstracts (0.99; 95% CrI = 0.99, 1.00). We noted strong secular trends in case definition accuracy.Conclusions: Administrative data remain a useful tool for conducting traumatic brain injury surveillance and epidemiologic research when measurement error is adjusted for. [ABSTRACT FROM AUTHOR]

Published
2018
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230. Improving patient safety and efficiency of medication reconciliation through the development and adoption of a computer-assisted tool with automated electronic integration of population-based community drug data: the RightRx project.

Author

Tamblyn, Robyn, Winslade, Nancy, Lee, Todd C., Motulsky, Aude, Meguerditchian, Ari, Bustillo, Melissa, Elsayed, Sarah, Buckeridge, David L., Couture, Isabelle, Qian, Christina J., Moraga, Teresa, and Huang, Allen

Abstract

Background and Objective: Many countries require hospitals to implement medication reconciliation for accreditation, but the process is resource-intensive, thus adherence is poor. We report on the impact of prepopulating and aligning community and hospital drug lists with data from population-based and hospital-based drug information systems to reduce workload and enhance adoption and use of an e-medication reconciliation application, RightRx.Methods: The prototype e-medical reconciliation web-based software was developed for a cluster-randomized trial at the McGill University Health Centre. User-centered design and agile development processes were used to develop features intended to enhance adoption, safety, and efficiency. RightRx was implemented in medical and surgical wards, with support and training provided by unit champions and field staff. The time spent per professional using RightRx was measured, as well as the medication reconciliation completion rates in the intervention and control units during the first 20 months of the trial.Results: Users identified required modifications to the application, including the need for dose-based prescribing, the role of the discharge physician in prescribing community-based medication, and access to the rationale for medication decisions made during hospitalization. In the intervention units, both physicians and pharmacists were involved in discharge reconciliation, for 96.1% and 71.9% of patients, respectively. Medication reconciliation was completed for 80.7% (surgery) to 96.0% (medicine) of patients in the intervention units, and 0.7% (surgery) to 82.7% of patients in the control units. The odds of completing medication reconciliation were 9 times greater in the intervention compared to control units (odds ratio: 9.0, 95% confidence interval, 7.4-10.9, P < .0001) after adjusting for differences in patient characteristics.Conclusion: High rates of medication reconciliation completion were achieved with automated prepopulation and alignment of community and hospital medication lists. [ABSTRACT FROM AUTHOR]

Published
2018
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231. The Relationship Between 2009 Pandemic H1N1 Influenza During Pregnancy and Preterm Birth: A Population-based Cohort Study.

Author

Fell, Deshayne B., Platt, Robert W., Basso, Olga, Wilson, Kumanan, Kaufman, Jay S., Buckeridge, David L., and Kwong, Jeffrey C.

Subjects
INFLUENZA epidemiology, COMMUNICABLE disease epidemiology, COMMUNICABLE diseases, EPIDEMICS, PREMATURE infants, INFLUENZA, LONGITUDINAL method, PREGNANCY complications, THIRD trimester of pregnancy, RESEARCH funding, COMORBIDITY, ACQUISITION of data, PROPORTIONAL hazards models, RETROSPECTIVE studies, INFLUENZA A virus, H1N1 subtype
Abstract

Background: Previous studies of maternal influenza illness and preterm birth have yielded inconsistent results. Our objective was to assess the association between 2009 pandemic H1N1 (pH1N1) influenza during pregnancy and preterm birth in a large obstetrical population.Methods: We linked a province-wide birth registry with health administrative databases to identify influenza-coded hospitalizations, emergency department visits, or physician visits among pregnant women during the 2009 H1N1 pandemic (our proxy for clinical pH1N1 influenza illness). Using Cox regression, we estimated adjusted hazard ratios (aHR) for preterm birth and spontaneous preterm birth treating influenza as a time-varying exposure.Results: Among 192,082 women with a singleton live birth, 2,925 (1.5%) had an influenza-coded health care encounter during the 2009 H1N1 pandemic. Compared with unexposed pregnancy time, there was no association between exposure to the pandemic, with or without clinical influenza illness, and preterm birth (no pH1N1 diagnosis: aHR = 1.0; 95% confidence interval [CI] = 0.98, 1.1; pH1N1 diagnosis: aHR = 1.0; 95% CI = 0.88, 1.2). Among women with preexisting medical conditions, influenza was associated with increased preterm birth (aHR = 1.5; 95% CI = 1.1, 2.2) and spontaneous preterm birth (aHR = 1.7; 95% CI = 1.1, 2.6), and these associations were strongest in the third trimester and when data were analyzed to allow for a transient acute effect of influenza.Conclusions: In the general obstetrical population, there was no association between pH1N1 influenza illness and preterm birth, but women with preexisting medical conditions known to increase the risk of influenza-associated morbidity were at elevated risk. [ABSTRACT FROM AUTHOR]

Published
2018
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233. Simulation Analysis Platform (SnAP): a Tool for Evaluation of Public Health Surveillance and Disease Control Strategies

Author

Buckeridge, David L., Jauvin, Christian, Okhmatovskaia, Anya, and Verma, Aman D.

Subjects
Evaluation Studies as Topic, Electronic Health Records, Humans, Computer Simulation, Public Health Surveillance, Articles, Software, Disease Outbreaks
Abstract

Increasingly, researchers use simulation to generate realistic population health data to evaluate surveillance and disease control methods. This evaluation approach is attractive because real data are often not available to describe the full range of population health trajectories that may occur. Simulation models, especially agent-based models, tend to have many parameters and it is often difficult for researchers to evaluate the effect of the multiple parameter values on model outcomes. In this paper, we describe Simulation Analysis Platform (SnAP) - a software infrastructure for automatically deploying and analyzing multiple runs of a simulation model in a manner that efficiently explores the influence of parameter uncertainty and random error on model outcomes. SnAP is designed to be efficient, scalable, extensible, and portable. We describe the design decisions taken to meet these requirements, present the design of the platform, and describe results from an example application of SnAP.

Published
2011

241. Reports on the 2015 AAAI Workshop Series

Author

Albrecht, Stefano V., primary, Beck, J. Christopher, additional, Buckeridge, David L., additional, Botea, Adi, additional, Caragea, Cornelia, additional, Chi, Chi‐hung, additional, Damoulas, Theodoros, additional, Dilkina, Bistra, additional, Eaton, Eric, additional, Fazli, Pooyan, additional, Ganzfried, Sam, additional, Giles, C. Lee, additional, Guillet, Sébastien, additional, Holte, Robert, additional, Hutter, Frank, additional, Koch, Thorsten, additional, Leonetti, Matteo, additional, Lindauer, Marius, additional, Machado, Marlos C., additional, Malitsky, Yuri, additional, Marcus, Gary, additional, Meijer, Sebastiaan, additional, Rossi, Francesca, additional, Shaban‐Nejad, Arash, additional, Thiebaux, Sylvie, additional, Veloso, Manuela, additional, Walsh, Toby, additional, Wang, Can, additional, Zhang, Jie, additional, and Zheng, Yu, additional

Published
2015
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242. Reports of the AAAI 2014 Conference Workshops

Author

Albrecht, Stefano V., primary, Barreto, André M. S., additional, Braziunas, Darius, additional, Buckeridge, David L., additional, Cuayáhuitl, Heriberto, additional, Dethlefs, Nina, additional, Endres, Markus, additional, Farahmand, Amir-massoud, additional, Fox, Mark, additional, Frommberger, Lutz, additional, Ganzfried, Sam, additional, Gil, Yolanda, additional, Guillet, Sébastien, additional, Hunter, Lawrence E., additional, Jhala, Arnav, additional, Kersting, Kristian, additional, Konidaris, George, additional, Lecue, Freddy, additional, McIlraith, Sheila, additional, Natarajan, Sriraam, additional, Noorian, Zeinab, additional, Poole, David, additional, Ronfard, Rémi, additional, Saffiotti, Alessandro, additional, Shaban-Nejad, Arash, additional, Srivastava, Biplav, additional, Tesauro, Gerald, additional, Uceda-Sosa, Rosario, additional, Van den Broeck, Guy, additional, Van Otterlo, Martijn, additional, Wallace, Byron C., additional, Weng, Paul, additional, Wiens, Jenna, additional, and Zhang, Jie, additional

Published
2015
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247. Guess Who's Not Coming to Dinner? Evaluating Online Restaurant Reservations for Disease Surveillance

Author

Nsoesie, Elaine O., Buckeridge, David L., Brownstein, John S., Nsoesie, Elaine O., Buckeridge, David L., and Brownstein, John S.

Abstract

Background: Alternative data sources are used increasingly to augment traditional public health surveillance systems. Examples include over-the-counter medication sales and school absenteeism. Objective: We sought to determine if an increase in restaurant table availabilities was associated with an increase in disease incidence, specifically influenza-like illness (ILI). Methods: Restaurant table availability was monitored using OpenTable, an online restaurant table reservation site. A daily search was performed for restaurants with available tables for 2 at the hour and at half past the hour for 22 distinct times: between 11:00 am-3:30 pm for lunch and between 6:00-11:30 PM for dinner. In the United States, we examined table availability for restaurants in Boston, Atlanta, Baltimore, and Miami. For Mexico, we studied table availabilities in Cancun, Mexico City, Puebla, Monterrey, and Guadalajara. Time series of restaurant use was compared with Google Flu Trends and ILI at the state and national levels for the United States and Mexico using the cross-correlation function. Results: Differences in restaurant use were observed across sampling times and regions. We also noted similarities in time series trends between data on influenza activity and restaurant use. In some settings, significant correlations greater than 70% were noted between data on restaurant use and ILI trends. Conclusions: This study introduces and demonstrates the potential value of restaurant use data for event surveillance.

Published
2014
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248. Decision Theoretic Analysis of Improving Epidemic Detection

Author

Izadi, Masoumeh T. and Buckeridge, David L.

Subjects
Anthrax, Decision Theory, Population Surveillance, Humans, Articles, Bioterrorism, Algorithms, Markov Chains, Decision Support Techniques, Disease Outbreaks
Abstract

The potentially catastrophic impact of an epidemic specially these due to bioterrorist attack, makes developing effective detection methods essential for public health. Current detection methods trade off reliability of alarms for early detection of outbreaks. The performance of these methods can be improved by disease-specific modeling techniques that take into account the potential costs and effects of an attack to provide optimal warnings and the cost and effectiveness of interventions. We study this optimization problem in the framework of sequential decision making under uncertainty. Our approach relies on estimating the future benefit of true alarms and the costs of false alarms. Using these quantities it identifies optimal decisions regarding the credibility of outputs from a traditional detection method at each point in time. The key contribution of this paper is to apply Partially Observable Markov Decision Processes (POMDPs) on outbreak detection methods for improving alarm function in the case of anthrax. We present empirical evidence illustrating that at a fixed specificity, the performance of detection methods with respect to sensitivity and timeliness is improved significantly by utilizing POMDPs in detection of anthrax attacks.

Published
2007

249. An evaluation model for syndromic surveillance: assessing the performance of a temporal algorithm

Author

Buckeridge, David L., Switzer, P., Owens, D., Siegrist, D., Pavlin, J., and Musen, M.

Subjects
Company systems management, Epidemics -- United States, Epidemics -- Safety and security measures, Epidemics -- Information management, Epidemiologic methods -- Research, Diseases -- Reporting, Diseases -- Methods
Abstract

Abstract Introduction: Syndromic surveillance offers the potential to rapidly detect outbreaks resulting from terrorism. Despite considerable experience with implementing syndromic surveillance, limited evidence exists to describe the performance of syndromic--surveillance [...]

Published
2005

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