BMC Medical Informatics and Decision Making最新文献

{"title":"Development of transient ischemic attack risk prediction model suitable for initializing a learning health system unit using electronic medical records.","authors":"Jian Wen, Tianmei Zhang, Shangrong Ye, Cheng Li, Ruobing Han, Ran Huang, Bairong Shen, Anjun Chen, Qinghua Li","doi":"10.1186/s12911-024-02767-x","DOIUrl":"10.1186/s12911-024-02767-x","url":null,"abstract":"<p><strong>Background: </strong>Patients with transient ischemic attack (TIA) face a significantly increased risk of stroke. However, TIA screening and early detection rates are low, especially in developing countries. This study aims to develop an inclusive and practical TIA risk prediction model using machine learning (ML) that performs well in both hospital and resource-limited clinic settings. This model is essential for initiating the first ML-enabled learning health system (LHS) unit designed for routine and equitable TIA screening and early detection across broad populations.</p><p><strong>Methods: </strong>Employing a novel protocol, this study first standardized data from a hospital's electronic medical records (EMR) to construct inclusive TIA risk prediction ML models using a data-centric approach. Subsequently, a quantitative distribution of TIA risk factors was applied in feature engineering to reduce the number of variables for a practical ML model. This refined model initiated a TIA ML-LHS unit that is capable of continuously updating with new EMR data from hospitals and clinics. Additionally, the practical model underwent external validation using data from another hospital.</p><p><strong>Results: </strong>The inclusive 150-variable ML models, derived from all available EMR variables for TIA, achieved a recall of 0.868 and an accuracy of 0.886 in predicting TIA risk. Further feature engineering produced a practical XGBoost model with 20 variables, maintaining acceptable performance of 0.855 recall and 0.796 accuracy. The initialized TIA ML-LHS unit, based on the practical model, achieved performance metrics of 0.830 recall, 0.726 precision, 0.816 ROC-AUC, and 0.812 accuracy. The model also performed well in external validation, confirming its effectiveness with patient data from different clinical settings.</p><p><strong>Conclusions: </strong>This study developed the first inclusive and practical TIA XGBoost model from full hospital EHR and initiated the first TIA risk prediction ML-LHS unit. This TIA model, which requires only 20 variables, enables the ML-LHS to serve not only patients in hospitals but also those in resource-limited clinics. These results have significant implications for expanding risk-based TIA screening in community and rural clinics, thereby enhancing early detection of TIA among underserved populations and improving health equity. The novel protocol used in this study is also applicable for initiating ML-LHS units for various preventable diseases, providing a new system-level approach to responsible AI development and applications.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"24 1","pages":"392"},"PeriodicalIF":3.3,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11657208/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142852896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Engaging adolescents and young adults in decisions about return of genomic research results: study protocol for a mixed-methods longitudinal clinical trial protocol.","authors":"Amy A Blumling, Michelle L McGowan, Cynthia A Prows, Kristin Childers-Buschle, Lisa J Martin, John A Lynch, Kevin R Dufendach, Ellen A Lipstein, Melinda Butsch Kovacic, William B Brinkman, Melanie F Myers","doi":"10.1186/s12911-024-02784-w","DOIUrl":"10.1186/s12911-024-02784-w","url":null,"abstract":"<p><strong>Background: </strong>To protect minors' future autonomy, professional organizations have historically discouraged returning predictive adult-onset genetic test results and carrier status to children. Recent clinical guidance diverges from this norm, suggesting that when minors have genomic sequencing performed for clinical purposes, parents and children should have the opportunity to learn secondary findings, including for some adult-onset conditions. While parents can currently opt in or out of receiving their child's secondary findings, the American Society of Human Genetics Workgroup on Pediatric Genetic and Genomic Testing suggests including adolescents in the decision-making process. However, it is not clear what factors young people consider when given the opportunity to learn genetic findings for themselves. In this manuscript, we report on the methods for a clinical trial that examines adolescents', young adults', and their parents' decisions about learning genomic information for the adolescent or young adult.</p><p><strong>Methods: </strong>We are enrolling assenting (ages 13-17) adolescents and consenting (ages 18-21) young adults in a prospective genomic screening study to assess the choices they make about receiving individual genomic results. Participants use an online tool to indicate whether they want to learn their personal genetic risk for specific preventable, treatable, and adult-onset conditions, as well as carrier status for autosomal recessive conditions. We are examining (1) how choices differ between adolescent and young adult cohorts (as well as between adolescents/young adults and parents) and (2) decisional conflict and stability across study timepoints. Results are returned based on participants' choices. Qualitative interviews with a subset of participants explore decisional stability, adolescent/young adult engagement with parents in decision-making, and the impact of learning pathogenic/likely pathogenic and autosomal recessive carrier results.</p><p><strong>Discussion: </strong>This study explores decision making and decision stability between adolescents and parents (where applicable), as well as the ethical implications and impact of return of clinical-grade genetic research results to adolescents and young adults. The results of this study will contribute empirical evidence to support best practices and guidance on engaging young people in genomic research studies and clinical care that offer return of results.</p><p><strong>Trial registration: </strong>ClinicalTrials.gov Identifier: NCT04481061. Registered 22 July 2020.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"24 1","pages":"391"},"PeriodicalIF":3.3,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11657641/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142852926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reduced-dose deep learning iterative reconstruction for abdominal computed tomography with low tube voltage and tube current.","authors":"Shumeng Zhu, Baoping Zhang, Qian Tian, Ao Li, Zhe Liu, Wei Hou, Wenzhe Zhao, Xin Huang, Yao Xiao, Yiming Wang, Rui Wang, Yuhang Li, Jian Yang, Chao Jin","doi":"10.1186/s12911-024-02811-w","DOIUrl":"10.1186/s12911-024-02811-w","url":null,"abstract":"<p><strong>Background: </strong>The low tube-voltage technique (e.g., 80 kV) can efficiently reduce the radiation dose and increase the contrast enhancement of vascular and parenchymal structures in abdominal CT. However, a high tube current is always required in this setting and limits the dose reduction potential. This study investigated the feasibility of a deep learning iterative reconstruction algorithm (Deep IR) in reducing the radiation dose while improving the image quality for abdominal computed tomography (CT) with low tube voltage and current.</p><p><strong>Methods: </strong>Sixty patients (male/female, 36/24; Age, 57.72 ± 10.19 years) undergoing the abdominal portal venous phase CT were randomly divided into groups A (100 kV, automatic exposure control [AEC] with reference tube-current of 213 mAs) and B (80 kV, AEC with reference of 130 mAs). Images were reconstructed via hybrid iterative reconstruction (HIR) and Deep IR (levels 1-5). The mean CT and standard deviation (SD) values of four regions of interest (ROI), i.e. liver, spleen, main portal vein and erector spinae at the porta hepatis level in each image serial were measured, and the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated. The image quality was subjectively scored by two radiologists using a 5-point criterion.</p><p><strong>Results: </strong>A significant reduction in the radiation dose of 69.94% (5.09 ± 0.91 mSv vs. 1.53 ± 0.37 mSv) was detected in Group B compared with Group A. After application of the Deep IR, there was no significant change in the CT value, but the SD gradually increased. Group B had higher CT values than group A, and the portal vein CT values significantly differed between the groups (P < 0.003). The SNR and CNR in Group B with Deep IR at levels 1-5 were greater than those in Group A and significantly differed when HIR and Deep IR were applied at levels 1-3 of HIR and Deep IR (P < 0.003). The subjective scores (distortion, clarity of the portal vein, visibility of small structures and overall image quality) with Deep IR at levels 4-5 in Group B were significantly higher than those in group A with HIR (P < 0.003).</p><p><strong>Conclusion: </strong>Deep IR algorithm can meet the clinical requirements and reduce the radiation dose by 69.94% in portal venous phase abdominal CT with a low tube voltage of 80 kV and a low tube current. Deep IR at levels 4-5 can significantly improve the image quality of the abdominal parenchymal organs and the clarity of the portal vein.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"24 1","pages":"389"},"PeriodicalIF":3.3,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11658360/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142853026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced forecasting of emergency department patient arrivals using feature engineering approach and machine learning.","authors":"Bruno Matos Porto, Flavio Sanson Fogliatto","doi":"10.1186/s12911-024-02788-6","DOIUrl":"10.1186/s12911-024-02788-6","url":null,"abstract":"<p><strong>Background: </strong>Emergency department (ED) overcrowding is an important problem in many countries. Accurate predictions of ED patient arrivals can help management to better allocate staff and medical resources. In this study, we investigate the use of calendar and meteorological predictors, as well as feature-engineered variables, to predict daily patient arrivals using datasets from eleven different EDs across three countries.</p><p><strong>Methods: </strong>Six machine learning (ML) algorithms were tested on forecasting horizons of 7 and 45 days. Three of them - Light Gradient Boosting Machine (LightGBM), Support Vector Machine with Radial Basis Function (SVM-RBF), and Neural Network Autoregression (NNAR) - were never before reported for predicting ED patient arrivals. Algorithms' hyperparameters were tuned through a grid-search with cross-validation. Prediction performance was assessed using fivefold cross-validation and four performance metrics.</p><p><strong>Results: </strong>The eXtreme Gradient Boosting (XGBoost) was the best-performing model on both prediction horizons, also outperforming results reported in past studies on ED arrival prediction. XGBoost and NNAR achieved the best performance in nine out of the eleven analyzed datasets, with MAPE values ranging from 5.03% to 14.1%. Feature engineering (FE) improved the performance of the ML algorithms.</p><p><strong>Conclusion: </strong>Accuracy in predicting ED arrivals, achieved through the FE approach, is key for managing human and material resources, as well as reducing patient waiting times and lengths of stay.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"24 1","pages":"377"},"PeriodicalIF":3.3,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11653554/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142852942","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving event prediction using general practitioner clinical judgement in a digital risk stratification model: a pilot study.","authors":"Emma Parry, Kamran Ahmed, Elizabeth Guest, Vijay Klaire, Abdool Koodaruth, Prasadika Labutale, Dawn Matthews, Jonathan Lampitt, Alan Nevill, Gillian Pickavance, Mona Sidhu, Kate Warren, Baldev M Singh","doi":"10.1186/s12911-024-02797-5","DOIUrl":"10.1186/s12911-024-02797-5","url":null,"abstract":"<p><strong>Background: </strong>Numerous tools based on electronic health record (EHR) data that predict risk of unscheduled care and mortality exist. These are often criticised due to lack of external validation, potential for low predictive ability and the use of thresholds that can lead to large numbers being escalated for assessment that would not have an adverse outcome leading to unsuccessful active case management. Evidence supports the importance of clinical judgement in risk prediction particularly when ruling out disease. The aim of this pilot study was to explore performance analysis of a digitally driven risk stratification model combined with GP clinical judgement to identify patients with escalating urgent care and mortality events.</p><p><strong>Methods: </strong>Clinically risk stratified cohort study of 6 GP practices in a deprived, multi-ethnic UK city. Initial digital driven risk stratification into Escalated and Non-escalated groups used 7 risk factors. The Escalated group underwent stratification using GP global clinical judgement (GCJ) into Concern and No concern groupings.</p><p><strong>Results: </strong>3968 out of 31,392 patients were data stratified into the Escalated group and further categorised into No concern (n = 3450 (10.9%)) or Concern (n = 518 (1.7%)) by GPs. The 30-day combined event rate (unscheduled care or death) per 1,000 was 19.0 in the whole population, 67.8 in the Escalated group and 168.0 in the Concern group (p < 0.001). The de-escalation effect of GP assessment into No Concern versus Concern was strongly negatively predictive (OR 0.25 (95%CI 0.19-0.33; p < 0.001)). The whole population ROC for the global approach (Non-escalated, GP No Concern, GP Concern) was 0.614 (0.592-0.637), p < 0.001, and the increase in the ROC area under the curve for 30-day events was all focused here (+ 0.4% (0.3-0.6%, p < 0.001), translating into a specific ROC c-statistic for GP GCJ of 0.603 ((0.565-0.642), p < 0.001).</p><p><strong>Conclusions: </strong>The digital only component of the model performed well but adding GP clinical judgement significantly improved risk prediction, particularly by adding negative predictive value.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"24 1","pages":"382"},"PeriodicalIF":3.3,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11654091/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142852954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An explainable analysis of diabetes mellitus using statistical and artificial intelligence techniques.","authors":"William Hoyos, Kenia Hoyos, Rander Ruiz, Jose Aguilar","doi":"10.1186/s12911-024-02810-x","DOIUrl":"10.1186/s12911-024-02810-x","url":null,"abstract":"<p><strong>Background: </strong>Diabetes mellitus (DM) is a chronic disease prevalent worldwide, requiring a multifaceted analytical approach to improve early detection and subsequent mitigation of morbidity and mortality rates. This research aimed to develop an explainable analysis of DM by combining sociodemographic and clinical data with statistical and artificial intelligence (AI) techniques.</p><p><strong>Methods: </strong>Leveraging a small dataset that includes sociodemographic and clinical profiles of diabetic and non-diabetic individuals, we employed a diverse set of statistical and AI models for predictive purposes and assessment of DM risk factors. The statistical tests used were Student's t-test and Chi-square, while the AI techniques were fuzzy cognitive maps (FCM), artificial neural networks (ANN), support vector machines (SVM), and XGBoost.</p><p><strong>Results: </strong>Our statistical models facilitated an in-depth exploration of variable associations, while the resulting AI models demonstrated exceptional efficacy in DM classification. In particular, the XGBoost model showed superior performance in accuracy, sensitivity and specificity with values of 1 for each of these metrics. On the other hand, the FCM stood out for its explainability capabilities by allowing an analysis of the variables involved in the prediction using scenario-based simulations.</p><p><strong>Conclusions: </strong>An integrated analysis of DM using a variety of methodologies is critical for timely detection of the disease and informed clinical decision-making.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"24 1","pages":"383"},"PeriodicalIF":3.3,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11654128/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142853023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The social experience of uncertainty: a qualitative analysis of emergency department care for suspected pneumonia for the design of decision support.","authors":"Peter Taber, Charlene Weir, Susan L Zickmund, Elizabeth Rutter, Jorie Butler, Barbara E Jones","doi":"10.1186/s12911-024-02805-8","DOIUrl":"10.1186/s12911-024-02805-8","url":null,"abstract":"<p><strong>Background: </strong>This study sought to understand the process of clinical decision-making for suspected pneumonia by emergency departments (ED) providers in Veterans Affairs (VA) Medical Centers. The long-term goal of this work is to create clinical decision support tools to reduce unwarranted variation in diagnosis and treatment of suspected pneumonia.</p><p><strong>Methods: </strong>Semi-structured qualitative interviews were conducted with 16 ED clinicians from 9 VA facilities demonstrating variation in antibiotic and hospitalization decisions. Interviews of ED providers focused on understanding decision making for provider-selected pneumonia cases and providers' organizational contexts.</p><p><strong>Results: </strong>Thematic analysis identified four salient themes: i) ED decision-making for suspected pneumonia is a social process; ii) the \"diagnosis drives treatment\" paradigm is poorly suited to pneumonia decision-making in the ED; iii) The unpredictability of the ED requires deliberate and effortful information management by providers in CAP decision-making; and iv) the emotional stakes and high uncertainty of pneumonia care drive conservative decision making.</p><p><strong>Conclusions: </strong>Ensuring CDS reflects the realities of clinical work as a socially organized process with high uncertainty may ultimately improve communication between ED and admitting providers, continuity of care and patient outcomes.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"24 1","pages":"386"},"PeriodicalIF":3.3,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11657753/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142853030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Challenges and solutions in implementing electronic prescribing in Iran's health system: a qualitative study.","authors":"Neda Borhani Moghani, Elahe Hooshmand, Marzie Zarqi, Marziyhe Meraji","doi":"10.1186/s12911-024-02737-3","DOIUrl":"10.1186/s12911-024-02737-3","url":null,"abstract":"<p><strong>Background: </strong>The use of electronic prescribing is recognized as a strategic tool for improving healthcare. Given the nationwide implementation of electronic prescribing systems initiated in 2020, this study aims to explore the challenges and solutions for implementing electronic prescribing in Iran's health system as a developing country.</p><p><strong>Methods: </strong>This qualitative study was conducted through interviews with physicians, pharmacy staff, and electronic prescribing representatives in 2023. Initially, three in-depth interviews were conducted to develop the interview questions, resulting in three separate interview guides for each participant group (supplementary file no.1). Participants were purposively selected, including 12 physicians, 15 electronic prescribing representatives, and 9 pharmacy staff members. Interviews continued until data saturation was reached. The interviews were recorded, transcribed, and analyzed using Inductive content analysis with MAXQDA version 10 software. To identify challenges, sessions were held, and a final list of challenges was categorized. In the final stage, expert panels including 3 researchers, 4 e-prescribing representatives, and 3 insurance experts were formed to propose solutions.</p><p><strong>Result: </strong>The challenges identified in this study were categorized into two main domains: \"Organizational Challenges\" and \"Systemic Challenges.\" Organizational challenges included issues related to insurance (16 cases), patient referrals (4 cases), stakeholder education and communication (6 cases), and supervision (8 cases). Systemic challenges included infrastructure problems (18 cases), user interface (UI) issues (14 cases), and database issues (10 cases). The primary challenges in implementing electronic prescribing were system downtime and sluggishness, internet connectivity issues, and the existence of multiple insurance systems. Expert panel discussions resulted in proposed solutions, including the uniform design of software by the Ministry of Health, the establishment of an integrated electronic referral system, conducting practical training sessions for physicians, and implementing electronic signatures.</p><p><strong>Conclusion: </strong>Electronic prescribing in Iran is still in its early stages and will inevitably face challenges and problems. Continuous monitoring of electronic prescribing systems is essential to address implementation issues promptly. Issues related to training insurance monitoring the user interface and database infrastructure were challenging. Overall, improvements in infrastructure, integration of insurance systems, implementation of electronic signatures, adherence to electronic prescribing standards, and provision of practical training are recommended.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"24 1","pages":"393"},"PeriodicalIF":3.3,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11657228/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142851898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Target informed client recruitment for efficient federated learning in healthcare.","authors":"Vincent Scheltjens, Lyse Naomi Wamba Momo, Wouter Verbeke, Bart De Moor","doi":"10.1186/s12911-024-02798-4","DOIUrl":"10.1186/s12911-024-02798-4","url":null,"abstract":"<p><strong>Background: </strong>Modern machine learning and deep learning methods have been widely incorporated in decision making processes in healthcare in the form of decision support mechanisms. In healthcare, data are abundant but typically not centrally available and, therefore, require some form of aggregation to facilitate training procedures. Aggregating sensitive data poses a significant privacy risk, which is why, both in Europe and the United States, legal frameworks regulate the treatment of such data. Whilst these measures protect the individual behind the data, they pose a significant challenge that results in extensive legal administration related to data sharing efforts. Federated learning (FL) offers a way to mitigate these challenges by allowing to learn models in distributed fashion, eliminating the need to aggregate data for the purpose of training. However, FL comes with a new set of challenges related to communication overhead, client selection and efficiency of the FL training procedure, among others.</p><p><strong>Methods: </strong>In this work, we extend on a previously proposed client recruitment approach by incorporating knowledge on the local hardware such that it becomes possible to recruit a subset of clients for the federation based on the construct of client-level representativeness, which is expressed in terms of the local target distribution divergence, sample size, and the underlying hardware.</p><p><strong>Results: </strong>We show that, for prominent, medical regression and classification tasks, the recruitment approach yields results that are on par, or better, compared to the central and federated approaches. The proposed approach requires a mere fraction of the data for training and reduces the training time by a factor of 3-4. In addition, we show that excluded clients can still significantly benefit from the resulting federated model through local fine-tuning.</p><p><strong>Conclusions: </strong>By expressing the representativeness of clients in function of the deviation in the local target distribution, the sample size and efficiency of the underlying hardware, we are able to define a recruitment approach that yields a subset of clients for the federation resulting in significantly reduced training time, without harming predictive performance, whilst improving the privacy preserving characteristics compared to the standard FL and central approaches.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"24 1","pages":"380"},"PeriodicalIF":3.3,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11653562/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142853028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and evaluation of a shared decision-making system for choosing the type of bariatric surgery.","authors":"Sahar Darnahal, Rita Rezaee, Somayyeh Zakerabasali","doi":"10.1186/s12911-024-02796-6","DOIUrl":"10.1186/s12911-024-02796-6","url":null,"abstract":"<p><strong>Introduction: </strong>Obesity is a multifactorial disease resulting from various environmental, genetic, and metabolic factors, affecting a large portion of the population. One of the most effective treatments for severe obesity is bariatric surgery. This research aims to develop a shared decision-making system that facilitates the selection of the appropriate type of bariatric surgery.</p><p><strong>Method: </strong>In this research, we designed and developed a prototype of a shared decision-making system to aid in choosing the type of bariatric surgery through three steps: a) identifying data requirements from a literature review, b) designing interfaces and prototyping, and c) conducting a usability evaluation.</p><p><strong>Results: </strong>Through a literature review of articles, books, and interviews with ten selected patients, the necessary clinical data and educational topics were identified and confirmed by nine surgeons. A prototype was developed using the web application \"Figma.\" We also analyzed the prototype using heuristic evaluation; \"helping users understand and recover from errors\" and \"confidentiality\" had the highest degrees of problem severity, with scores of 3.3 and 3.5, respectively.</p><p><strong>Conclusion: </strong>The developed prototype demonstrated an acceptable level of usability. This system can facilitate shared decision-making and help structure education for patients seeking bariatric surgery.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"24 1","pages":"385"},"PeriodicalIF":3.3,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11657857/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142852895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}