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Background: In clinical research on multifactorial diseases such as atopic dermatitis, data-driven medical research has become more widely used as means to clarify diverse pathological conditions and to realize precision medicine. However, modern clinical data, characterized as large-scale, multimodal, and multi-center, causes difficulties in data integration and management, which limits productivity in clinical data science. Methods: We designed a generic data management flow to collect, cleanse, and integrate data to handle different types of data generated at multiple institutions by 10 types of clinical studies. We developed MeDIA (Medical Data Integration Assistant), a software to browse the data in an integrated manner and extract subsets for analysis. Results: MeDIA integrates and visualizes data and information on research participants obtained from multiple studies. It then provides a sophisticated interface that supports data management and helps data scientists retrieve the data sets they need. Furthermore, the system promotes the use of unified terms such as identifiers or sampling dates to reduce the cost of pre-processing by data analysts. We also propose best practices in clinical data management flow, which we learned from the development and implementation of MeDIA. Conclusions: The MeDIA system solves the problem of multimodal clinical data integration, from complex text data such as medical records to big data such as omics data from a large number of patients. The system and the proposed best practices can be applied not only to allergic diseases but also to other diseases to promote data-driven medical research.

The aim of the study is to show whether it is possible to predict infectious disease outbreaks early, by using machine learning. This study was carried out following the guidelines of the Cochrane Collaboration and the meta-analysis of observational studies in epidemiology and the preferred reporting items for systematic reviews and meta-analyses. The suitable bibliography on PubMed/Medline and Scopus was searched by combining text, words, and titles on medical topics. At the end of the search, this systematic review contained 75 records. The studies analyzed in this systematic review demonstrate that it is possible to predict the incidence and trends of some infectious diseases; by combining several techniques and types of machine learning, it is possible to obtain accurate and plausible results.

Important pieces of information related to patient symptoms and diagnosis are often written in free-text form in clinical texts. To utilize these texts, information extraction using natural language processing is required. This study evaluated the performance of named entity recognition (NER) and relation extraction (RE) using machine-learning methods. The Japanese case report corpus was used for this study, which had 113 types of entities and 36 types of relations that were manually annotated. There were 183 cases comprising 2,194 sentences after preprocessing. In addition, a machine learning model based on bidirectional encoder representations from transformers was used. The results revealed that the maximum micro-averaged F1 scores of NER and RE were 0.912 and 0.759, respectively. The results of this study are comparable to those of previous studies. Hence, these results could be of substantial baseline accuracy. [ABSTRACT FROM AUTHOR]

PURPOSE:Computational fluid dynamics have paradigm shifting potential in understanding the physiological flow of fluids in the human body. This translational branch of engineering has already made an important clinical impact on the study of cardiovascular disease. We evaluated the feasibility and applicability of computational fluid dynamics to model urine flow. MATERIALS AND METHODS:We prepared a computational fluid dynamics model using an idealized male genitourinary system. We created 16 hypothetical urethral stricture scenarios as a test bed. Standard parameters of urine such as pressure, temperature and viscosity were applied as well as typical assumptions germane to fluid dynamic modeling. We used ABAQUS/CAE 6.14 (Dassault Systèmes®) with a direct unsymmetrical solver with standard (FC3D8) 3D brick 8Node elements for model generation. RESULTS:The average flow rate in urethral stricture disease as measured by our model was 5.97 ml per second (IQR 2.2-10.9). The model predicted a flow rate of 2.88 ml per second for a single 5Fr stricture in the mid bulbar urethra when assuming all other variables constant. The model demonstrated that increasing stricture diameter and bladder pressure strongly impacted urine flow while stricture location and length, and the sequence of multiple strictures had a weaker impact. CONCLUSIONS:We successfully created a computational fluid dynamics model of an idealized male urethra with varied types of urethral strictures. The resultant flow rates were consistent with the literature. The accuracy of modeling increasing bladder pressure should be improved by future iterations. This technology has vast research and clinical potential.

The aim of the study is to show whether it is possible to predict infectious disease outbreaks early, by using machine learning. This study was carried out following the guidelines of the Cochrane Collaboration and the meta-analysis of observational studies in epidemiology and the preferred reporting items for systematic reviews and meta-analyses. The suitable bibliography on PubMed/Medline and Scopus was searched by combining text, words, and titles on medical topics. At the end of the search, this systematic review contained 75 records. The studies analyzed in this systematic review demonstrate that it is possible to predict the incidence and trends of some infectious diseases; by combining several techniques and types of machine learning, it is possible to obtain accurate and plausible results. [ABSTRACT FROM AUTHOR]

BackgroundInformatics for Integrating Biology and the Bedside (i2b2) is an open source clinical data analytics platform used at over 200 healthcare institutions for querying patient data. The i2b2 platform has several components with numerous dependencies and configuration parameters, which renders the task of installing or upgrading i2b2 a challenging one. Even with the availability of extensive documentation and tutorials, new users often require several weeks to correctly install a functional i2b2 platform. The goal of this work is to simplify the installation and upgrade process for i2b2. Specifically, we have containerized the core components of the platform, and evaluated the containers for ease of installation.ResultsWe developed three Docker container images: WildFly, database, and web, to encapsulate the three major deployment components of i2b2. These containers isolate the core functionalities of the i2b2 platform, and work in unison to provide its functionalities. Our evaluations indicate that i2b2 containers function successfully on the Linux platform. Our results demonstrate that the containerized components work out-of-the-box, with minimal configuration.ConclusionsContainerization offers the potential to package the i2b2 platform components into standalone executable packages that are agnostic to the underlying host operating system. By releasing i2b2 as a Docker container, we anticipate that users will be able to create a working i2b2 hive installation without the need to download, compile, and configure individual components that constitute the i2b2 cells, thus making this platform accessible to a greater number of institutions.

Objective: The German Health Data Lab is going to provide access to German statutory health insurance claims data ranging from 2009 to the present for research purposes. Due to evolving data formats within the German Health Data Lab, there is a need to standardize this data into a Common Data Model to facilitate collaborative health research and minimize the need for researchers to adapt to multiple data formats. For this purpose we selected transforming the data to the Observational Medical Outcomes Partnership Common Data Model., Methods: We developed an Extract, Transform, and Load (ETL) pipeline for two distinct German Health Data Lab data formats: Format 1 (2009-2016) and Format 3 (2019 onwards). Due to the identical format structure of Format 1 and Format 2 (2017 -2018), the ETL pipeline of Format 1 can be applied on Format 2 as well. Our ETL process, supported by Observational Health Data Sciences and Informatics tools, includes specification development, SQL skeleton creation, and concept mapping. We detail the process characteristics and present a quality assessment that includes field coverage and concept mapping accuracy using example data., Results: For Format 1, we achieved a field coverage of 92.7%. The Data Quality Dashboard showed 100.0% conformance and 80.6% completeness, although plausibility checks were disabled. The mapping coverage for the Condition domain was low at 18.3% due to invalid codes and missing mappings in the provided example data. For Format 3, the field coverage was 86.2%, with Data Quality Dashboard reporting 99.3% conformance and 75.9% completeness. The Procedure domain had very low mapping coverage (2.2%) due to the use of mocked data and unmapped local concepts The Condition domain results with 99.8% of unique codes mapped. The absence of real data limits the comprehensive assessment of quality., Conclusion: The ETL process effectively transforms the data with high field coverage and conformance. It simplifies data utilization for German Health Data Lab users and enhances the use of OHDSI analysis tools. This initiative represents a significant step towards facilitating cross-border research in Europe by providing publicly available, standardized ETL processes (https://github.com/FraunhoferMEVIS/ETLfromHDLtoOMOP) and evaluations of their performance., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2025 Finster et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Introduction: Diabetes mellitus (DM) is increasingly recognized as a possible consequence of statin therapy. Secondary analysis of randomized clinical trials and limited observational cohort analyses have suggested that women may be more likely than men to experience statin-associated DM. No analyses of real-world drug safety data addressing this question have been published., Research Design and Methods: This was a retrospective pharmacovigilance analysis of spontaneously reported adverse drug events (ADEs) submitted to the Food and Drug Administration Adverse Event Reporting System between January 1997 through December 2023. We analyzed cases that mentioned atorvastatin, fluvastatin, lovastatin, pitavastatin, pravastatin, rosuvastatin, or simvastatin in aggregate as well as cases reporting atorvastatin, pravastatin, rosuvastatin, simvastatin individually. DM events were identified using the Medical Dictionary for Regulatory Activities. We used the proportional reporting ratio to identify increased rates of statin-associated DM events in women and men compared with all other medications, and the reporting OR to compare reporting rates in women versus men., Results: A total of 18,294,814 ADEs were reported during the study period. Among statin-associated ADEs, 14,874/519,209 (2.9%) reports mentioned DM in women compared with 7,411/489,453 (1.5%) in men, which were both significantly higher than background (0.6%). Statins were the primary-suspected or secondary-suspected cause of the ADE significantly more often in women than men (60 vs 30%), and reporting rates were disproportionately higher in women than in men for all statins. (reporting OR 1.9 (95% CI 1.9 to 2.0)). The largest difference in reporting of statin-associated DM between women and women was observed with atorvastatin., Conclusions: Analysis of post-marketing spontaneous ADE reports demonstrated a higher reporting rate of DM-associated with statin use compared with other medications with a significantly higher reporting rate in women compared with men. Future studies should consider mechanisms of statin-associated DM moderated by sex., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Clinical cancer pathways help standardize healthcare delivery to optimize patient outcomes and health system costs. However, population‐level measurement of concordance between standardized pathways and actual care received is lacking. Two measures of pathway concordance were developed for a simplified colon cancer pathway map for Stage II‐III colon cancer patients in Ontario, Canada: a cumulative count of concordant events (CCCE) and the Levenshtein algorithm. Associations of concordance with patient survival were estimated using Cox proportional hazards models adjusted for patient characteristics and time‐dependent cancer‐related activities. Models were compared and the impact of including concordance scores was quantified using the likelihood ratio chi‐squared test. The ability of the measures to discriminate between survivors and decedents was compared using the C‐index. Normalized concordance scores were significantly associated with patient survival in models for cancer stage—a 10% increase in concordance for Stage II patients resulted in a CCCE score adjusted hazard ratio (aHR) of death of 0.93, 95% CI 0.88‐0.98 and a Levenshtein score aHR of 0.64, 95% CI 0.60‐0.67. A similar relationship was found for Stage III patients—a 10% increase in concordance resulted in a CCCE aHR of 0.85, 95% CI 0.81‐0.88 and a Levenshtein aHR of 0.78, 95% CI, 0.74‐0.81. Pathway concordance can be used as a tool for health systems to monitor deviations from established clinical pathways. The Levenshtein score better characterized differences between actual care and clinical pathways in a population, was more strongly associated with survival and demonstrated better patient discrimination. [ABSTRACT FROM AUTHOR]

ObjectiveTo develop a multivariate method for quantifying the population representativeness across related clinical studies and a computational method for identifying and characterizing underrepresented subgroups in clinical studies.MethodsWe extended a published metric named Generalizability Index for Study Traits (GIST) to include multiple study traits for quantifying the population representativeness of a set of related studies by assuming the independence and equal importance among all study traits. On this basis, we compared the effectiveness of GIST and multivariate GIST (mGIST) qualitatively. We further developed an algorithm called "Multivariate Underrepresented Subgroup Identification" (MAGIC) for constructing optimal combinations of distinct value intervals of multiple traits to define underrepresented subgroups in a set of related studies. Using Type 2 diabetes mellitus (T2DM) as an example, we identified and extracted frequently used quantitative eligibility criteria variables in a set of clinical studies. We profiled the T2DM target population using the National Health and Nutrition Examination Survey (NHANES) data.ResultsAccording to the mGIST scores for four example variables, i.e., age, HbA1c, BMI, and gender, the included observational T2DM studies had superior population representativeness than the interventional T2DM studies. For the interventional T2DM studies, Phase I trials had better population representativeness than Phase III trials. People at least 65years old with HbA1c value between 5.7% and 7.2% were particularly underrepresented in the included T2DM trials. These results confirmed well-known knowledge and demonstrated the effectiveness of our methods in population representativeness assessment.ConclusionsmGIST is effective at quantifying population representativeness of related clinical studies using multiple numeric study traits. MAGIC identifies underrepresented subgroups in clinical studies. Both data-driven methods can be used to improve the transparency of design bias in participation selection at the research community level.

Purpose: Computational fluid dynamics (CFD) has been used successfully in cardiovascular system research to analyze the physiological processes inside vessels. We evaluated the hydraulic information of urine through the lower urinary tract in a patient with posterior urethral valve (PUV) before and after valve ablation by CFD. Methods: A set of models of the lower urinary tract were developed based on geometrical data obtained by cystoscopy and voiding cystourethrography. Simulated assumptions and conditions were applied according to prior studies and urodynamic results. We used Fluent CFD 19.0 (Ansys Inc., USA) to compute the velocity and pressure of the fluid regions. The simplification of Bernoulli's formula was applied afterward to calculate the hydraulic energy of different positions. Results: The urine flow rates of the NORMALst, the PUVst, and the POSTst at 5000 Pa were 18.08 ml/s, 11.14 ml/s, and 12.16 ml/s, respectively. Precipitous pressure change was observed around the valve in the PUVst, and the abnormal change was concentrated in the dilated urethra in the POSTst. Major energy dissipations were generated around the valve and the dilated urethra in the PUVst. The energy loss that occurred in the dilated urethra did not improve after the operation. Conclusions: Our findings are probably indicative of the hydrodynamics changes in the dilated urethra in PUV and need to be confirmed through more improved CFD models in the future. CFD may revolutionize pediatric urologists' perception in the management of urinary disease. [ABSTRACT FROM AUTHOR]

Background and Purpose: Drainage of obstructed kidney attributable to extrinsic ureteral obstruction (EUO), required to prevent renal damage, is often achieved using Double-J ureteral stents. However, these stents fail frequently, and there is considerable debate regarding what stent size, type, and configuration offer the best option for sustained drainage. In this study, we examine the impact of stent diameter and choice of single/tandem configuration, subject to EUO and various degrees of stent occlusion, on stent failure. Materials and Methods: Computational fluid dynamics simulations and an in vitro ureter-stent experiment enabled quantification of flow behavior in stented ureters subject to EUO and stent occlusions. Various single and tandem stents under EUO were considered. In each simulation and experiment, changes in renal pressure were monitored for different degrees of stent lumen occlusion, and onset of stent failure as well as simulated distributions of fluid flow between stent and ureter lumina were determined. Results: For an encircling EUO that completely obstructs the ureter lumen, with or without partial stent occlusion, the choice of stent size/configuration has little effect on renal pressure. The pressure increases significantly for ∼90% stent lumen occlusion, with failure at >95% occlusion, independent of stent diameter or a tandem configuration, and with little influence of occlusion length along the stent. Conclusions: Stent failure rate is independent of stent diameter or single/tandem configuration, for the same percentage of stent lumen occlusion, in this model. Stent failure incidence may decrease for larger diameter stents and tandem configurations, because of the larger luminal area. [ABSTRACT FROM AUTHOR]

Mitochondrial biology is the sum of diverse phenomena from molecular profiles to physiological functions. A mechanistic understanding of mitochondria in disease development, and hence the future prospect of clinical translations, relies on a systems-level integration of expertise from multiple fields of investigation. Upon the successful conclusion of a recent National Institutes of Health, National Heart, Lung, and Blood Institute initiative on integrative mitochondrial biology in cardiovascular diseases, we reflect on the accomplishments made possible by this unique interdisciplinary collaboration effort and exciting new fronts on the study of these remarkable organelles.

Objectives: Quantum noise is a random process in X-ray-based imaging systems. We addressed and measured the uncertainty of radiomics features against this quantum noise in computed tomography (CT) images. Methods: A clinical multi-detector CT scanner, two homogeneous phantom sets, and four heterogeneous samples were used. A solid tumor tissue removed from a male BALB/c mouse was included. We the placed phantom sets on the CT scanning table and repeated 20 acquisitions with identical imaging settings. Regions of interest were delineated for feature extraction. Statistical quantities—average, standard deviation, and percentage uncertainty—were calculated from these 20 repeated scans. Percentage uncertainty was used to measure and quantify feature stability against quantum noise. Twelve radiomics features were measured. Random noise was added to study the robustness of machine learning classifiers against feature uncertainty. Results: We found the ranges of percentage uncertainties from homogeneous soft tissue phantoms, homogeneous bone phantoms, and solid tumor tissue to be 0.01–2138%, 0.02–15%, and 0.18–16%, respectively. Overall, it was found that the CT features ShortRunHighGrayLevelEmpha (SRHGE) (0.01–0.18%), ShortRunLowGrayLevelEmpha (SRLGE) (0.01–0.41%), LowGrayLevelRunEmpha (LGRE) (0.01–0.39%), and LongRunLowGrayLevelEmpha (LRLGE) (0.02–0.66%) were the most stable features against the inherent quantum noise. The most unstable features were cluster shade (1–2138%) and max probability (1–16%). The impact of random noise to the prediction accuracy by different machine learning classifiers was found to be between 0 and 12%. Conclusions: Twelve features were used for uncertainty measurements. The upper and lower bounds of percentage uncertainties were determined. The quantum noise effect on machine learning classifiers is model dependent. Key Points: • Quantum noise is a random process and is intrinsic to X-ray-based imaging systems. This inherent quantum noise creates unpredictable fluctuations in the gray-level intensities of image pixels. Extra cautions and further validations are strongly recommended when unstable radiomics features are selected by a predictive model for disease classification or treatment outcome prognosis. • We addressed and used the statistical quantity of percentage uncertainty to measure the uncertainty of radiomics features against the inherent quantum noise in computed tomography (CT) images. • A clinical multi-detector CT scanner, two homogeneous phantom sets, and four heterogeneous samples were used in the stability measurement. A solid tumor tissue removed from a male BALB/c mouse was included in the heterogeneous sample. [ABSTRACT FROM AUTHOR]

ObjectivesConventional radiological processes have been replaced by digital images and information technology systems within South Africa and other developing countries. Picture Archiving and Communication Systems (PACS) technology offers many benefits to institutions, medical personnel and patients; however, the implementation of such systems can be a challenging task. It has been documented that South Africa has been using PACS for more than a decade in public hospitals with moderate success. The aim of this study was to identify and describe the PACS challenges endured by PACS vendors during implementation in the South African public healthcare sector.MethodsThis was achieved by engaging in a methodological approach that was qualitative in nature collecting data through semi structured interviews from 10 PACS experts/participants which were later analysed qualitatively.ResultsThe findings show that PACS vendors have countless challenges, some of which include space, insufficient infrastructure, image storage capacity, system maturity and vendor related concerns. It was clear that the PACS experts readily offered contextually appropriate descriptions of their encounters during PACS implementations in South African public healthcare institutions.ConclusionsPACS vendors anticipate these challenges when facing a public healthcare institution and it is recommended that the hospital management and potential PACS stakeholders be made aware of these challenges to mitigate their effects and aid in a successful implementation.

Digital pathology (the technology whereby glass histology slides are scanned at high resolution, digitised, stored and shared with pathologists, who can view them using microscopy software on a screen) is transforming the delivery of clinical diagnostic pathology services around the world. In addition to adding value to clinical histopathology practice, digital histology slides provide a versatile medium to achieve the educational needs of a variety of learners including undergraduate students, postgraduate doctors in training and those pursuing continuing professional development portfolios. In this guide, we will review the principal use cases for digital slides in training and education and I will share tips for successful use of digital pathology to support a range of learners based on experience gathered at Leeds Teaching Hospitals National Health Service Trust and the National Pathology Imaging Co-Operative during the last 5 years of digital slide usage., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY. Published by BMJ.)

Copyright of Annali di Igiene, Medicina Preventiva e di Comunità is the property of Societa Editrice Universo s.r.l. 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.)

Important pieces of information related to patient symptoms and diagnosis are often written in free-text form in clinical texts. To utilize these texts, information extraction using natural language processing is required. This study evaluated the performance of named entity recognition (NER) and relation extraction (RE) using machine-learning methods. The Japanese case report corpus was used for this study, which had 113 types of entities and 36 types of relations that were manually annotated. There were 183 cases comprising 2,194 sentences after preprocessing. In addition, a machine learning model based on bidirectional encoder representations from transformers was used. The results revealed that the maximum micro-averaged F1 scores of NER and RE were 0.912 and 0.759, respectively. The results of this study are comparable to those of previous studies. Hence, these results could be of substantial baseline accuracy.

During surgical procedures, it is important that the personnel (surgeon, residents, or assistants) interact with the patient avoiding any physical contact with equipment and materials that might have not been appropriately sterilized. This is done in order to prevent patient’s infections and complications after surgery. With the increased availability of diagnostic images, this technology has become indispensable in operating rooms but to maintain asepsis control of computer equipment on which the visualization programs are executed is not always possible, hindering access to personnel to information contained in the images. This paper describes the development of a system that allows the personnel to manipulate a medical imaging display program using gestures, avoiding the surgeon or the nurse to have a direct contact with the computer. The system, which requires a computer with 3D-Slicer software and Leap Motion (LM) device, allows through gestures made with the hands, to access the basic operations such as the movement between sections of a volume, to change the image size and the anatomical plane visualization; operations that are essential to the surgeon for the spatial location and decision making.

Background: Machine learning tools identify patients with blood counts indicating greater likelihood of colorectal cancer and warranting colonoscopy referral.Aims: To validate a machine learning colorectal cancer detection model on a US community-based insured adult population.Methods: Eligible colorectal cancer cases (439 females, 461 males) with complete blood counts before diagnosis were identified from Kaiser Permanente Northwest Region's Tumor Registry. Control patients (n = 9108) were randomly selected from KPNW's population who had no cancers, received at ≥1 blood count, had continuous enrollment from 180 days prior to the blood count through 24 months after the count, and were aged 40-89. For each control, one blood count was randomly selected as the pseudo-colorectal cancer diagnosis date for matching to cases, and assigned a "calendar year" based on the count date. For each calendar year, 18 controls were randomly selected to match the general enrollment's 10-year age groups and lengths of continuous enrollment. Prediction performance was evaluated by area under the curve, specificity, and odds ratios.Results: Area under the receiver operating characteristics curve for detecting colorectal cancer was 0.80 ± 0.01. At 99% specificity, the odds ratio for association of a high-risk detection score with colorectal cancer was 34.7 (95% CI 28.9-40.4). The detection model had the highest accuracy in identifying right-sided colorectal cancers.Conclusions: ColonFlag® identifies individuals with tenfold higher risk of undiagnosed colorectal cancer at curable stages (0/I/II), flags colorectal tumors 180-360 days prior to usual clinical diagnosis, and is more accurate at identifying right-sided (compared to left-sided) colorectal cancers. [ABSTRACT FROM AUTHOR]

Background: Identification of statistically reliable outcomes for comparison among anaesthetists is challenging. Time-weighted intraoperative mean arterial pressure <65 mm Hg (AUC 65 ) is associated with increased odds for myocardial damage. We explored retrospectively whether such hypotension before incision was statistically reliable for peer comparison.Methods: We retrieved electronic data between 2006 and 2015 at a tertiary care, academic hospital in the USA for patients at risk for myocardial damage (inpatient after surgery, ASA physical status ≥III, ≥50 yr of age, and case duration ≥60 min). We determined the percentage of anaesthetists comparable based on caseload and case-mix. The AUC 65 was compared amongst anaesthetists supervising ≥100 cases involving at-risk patients during the last 12 months.Results: Only 14.1% [95% confidence interval (CI) 13.6-14.5%] of cases involved patients who were 'at risk' during the 10 yr study period. A yearly average of 49 ( sd 6) anaesthetists supervised ≥100 cases of any type, of whom only 52% (95% CI 47.1-56.0%) supervised ≥100 cases involving at-risk patients. Thus, nearly half the anaesthetists would have been excluded from peer comparison. During the last 12 months, there were two outliers among 34 evaluable anaesthetists ( P <0.05, controlling for false discovery). However, their contribution to total hypotension amongst cases for all patients was small, because hypotension was widely distributed (e.g. 80% of hypotension attributable to 61.8% of anaesthetists, 95% CI 59.8-63.7%). There was no relationship between the AUC 65 and propofol induction dose.Conclusions: The AUC 65 of time-weighted pre-incision hypotension is not a suitable metric for comparing anaesthetists. There were few at-risk patients, half the anaesthetists were not evaluable because of their case-mix and caseload, and hypotension was widely distributed. [ABSTRACT FROM AUTHOR]

Copyright of Revista Facultad de Ingeniería Universidad de Antioquia is the property of Universidad de Antioquia 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.)

Data linkage brings together information from various sources, including routinely collected administrative data or data from different research studies, to create a new, richer dataset. It provides insights into complex relationships between health and outcomes and evidence pathways to good health. However, when considering data linkage, there are several processes and practicality aspects that need to be explored. Some of these include understanding the costs, complexity of linkage, data storage requirements, required applications, and time lags. Taking these practicalities into consideration will lead to a more efficient process for data linkage., Competing Interests: Conflict of interest: None declared., (© The Author(s) 2023. Published by Oxford University Press on behalf of the European Society of Cardiology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: Poor assessment of anaesthetic depth (AD) has led to overdosing or underdosing of the anaesthetic agent, which requires continuous monitoring to avoid complications. The evaluation of the central nervous system activity and autonomic nervous system could provide additional information on the monitoring of AD during surgical procedures., Methods: Observational analytical single-centre study, information on biological signals was collected during a surgical procedure under general anaesthesia for signal preprocessing, processing and postprocessing to feed a pattern classifier and determine AD status of patients. The development of the electroencephalography index was carried out through data processing and algorithm development using MATLAB V.8.1., Results: A total of 25 men and 35 women were included, with a total time of procedure average of 109.62 min. The results show a high Pearson correlation between the Complexity Brainwave Index and the indices of the entropy module. A greater dispersion is observed in the state entropy and response entropy indices, a partial overlap can also be seen in the boxes associated with deep anaesthesia and general anaesthesia in these indices. A high Pearson correlation might be explained by the coinciding values corresponding to the awake and general anaesthesia states. A high Pearson correlation might be explained by the coinciding values corresponding to the awake and general anaesthesia states., Conclusion: Biological signal filtering and a machine learning algorithm may be used to classify AD during a surgical procedure. Further studies will be needed to confirm these results and improve the decision-making of anaesthesiologists in general anaesthesia., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Data drift refers to differences between the data used in training a machine learning (ML) model and that applied to the model in real-world operation. Medical ML systems can be exposed to various forms of data drift, including differences between the data sampled for training and used in clinical operation, differences between medical practices or context of use between training and clinical use, and time-related changes in patient populations, disease patterns, and data acquisition, to name a few. In this article, we first review the terminology used in ML literature related to data drift, define distinct types of drift, and discuss in detail potential causes within the context of medical applications with an emphasis on medical imaging. We then review the recent literature regarding the effects of data drift on medical ML systems, which overwhelmingly show that data drift can be a major cause for performance deterioration. We then discuss methods for monitoring data drift and mitigating its effects with an emphasis on pre- and post-deployment techniques. Some of the potential methods for drift detection and issues around model retraining when drift is detected are included. Based on our review, we find that data drift is a major concern in medical ML deployment and that more research is needed so that ML models can identify drift early, incorporate effective mitigation strategies and resist performance decay., Competing Interests: Conflicts of interestNone.

Information on the oral health condition of the target population is required to enable the development of policy strategies for oral health promotion. This information needs to be substantiated by reliable data obtained through regular oral health assessments. Countries around the world have set up oral health data-registration systems that monitor the oral health of the population. These systems are either integrated in the public oral health care service or in national surveys conducted on a regular basis. This paper describes the conception and development of a Case Report Form for oral health assessments and introduces a recently developed electronic data-registration system for data capture in oral health surveys. The conception and development of a Case Report Form poses a number of challenges to be overcome. In addition to ensuring the scientific quality of its contents, several requirements need to be met. In the framework of national oral health surveys, handwritten data capture has proven accurate, but entails an important workload related to the printing and transporting of the forms, data transfer and storage of the forms, as well as the time required to perform these tasks. On the other hand, electronic data capture enables time saving and better performance. However, the advantages of this system may not be fully acknowledged by general practitioners, and their motivation to employ information and communication technologies may need to be encouraged. In the long term, the inclusion of electronic data registration in university training is probably the best strategy to achieve this.

The objective of this study was to develop a fuzzy model to estimate the possibility of neonatal mortality. A computing model was built, based on the fuzziness of the following variables: newborn birth weight, gestational age at delivery, Apgar score, and previous report of stillbirth. The inference used was Mamdani's method and the output was the risk of neonatal death given as a percentage. 24 rules were created according to the inputs. The validation model used a real data file with records from a Brazilian city. The receiver operating characteristic (ROC) curve was used to estimate the accuracy of the model, while average risks were compared using the Student t test. MATLAB 6.5 software was used to build the model. The average risks were smaller in survivor newborn (p < 0.001). The accuracy of the model was 0.90. The higher accuracy occurred with risk below 25%, corresponding to 0.70 in respect to sensitivity, 0.98 specificity, 0.99 negative predictive value and 0.22 positive predictive value. The model showed a good accuracy, as well as a good negative predictive value and could be used in general hospitals.O objetivo do artigo foi avaliar o uso da lógica fuzzy para estimar possibilidade de óbito neonatal. Desenvolveu-se um modelo computacional com base na teoria dos conjuntos fuzzy, tendo como variáveis peso ao nascer, idade gestacional, escore de Apgar e relato de natimorto. Empregou-se o método de inferência de Mamdani, e a variável de saída foi o risco de morte neonatal. Criaram-se 24 regras de acordo com as variáveis de entrada, e a validação do modelo utilizou um banco de dados real de uma cidade brasileira. A acurácia foi estimada pela curva ROC; os riscos foram comparados pelo teste t de Student. O programa MATLAB 6.5 foi usado para construir o modelo. Os riscos médios foram menores para os que sobreviveram (p < 0,001). A acurácia do modelo foi 0,90. A maior acurácia foi com possibilidade de risco igual ou menor que 25% (sensibilidade = 0,70, especificidade = 0,98, valor preditivo negativo = 0,99 e valor preditivo positivo = 0,22). O modelo mostrou acurácia e valor preditivo negativo bons, podendo ser utilizado em hospitais gerais.

The archiving of whole slide images represents a hurdle to digital pathology implementation largely because of the amount of data generated. The retention of glass slides is currently recommended for a minimum of 10 years, but it is for individual departments to determine how digital images are archived and for how long. In a retrospective study, we examined the combination of Systemised Nomenclature of Medicine (SNOMED) codes allocated to cases reported between July 2011 and December 2015 and recalled more than 12 months after diagnosis in comparison to non-recalled cases.Our results show that 0.2% of cases are recalled after 12 months, and SNOMED code combinations can be used to identify which cases are likely to be recalled and which are not. This approach could reduce the number of cases archived by 62% and still ensure all cases likely to be recalled remain in the archive., Competing Interests: Competing interests: DRJS is co-owner, director and shareholder of Histofy AI. All the other contributors do not have conflicts of interest to declare., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Competing Interests: Competing interests: None.