Journal of the American Medical Informatics Association最新文献

{"title":"Use of All of Us data to increase health literacy and research skills in high school students.","authors":"Katrina Go Yamazaki, Amy Taylor, Asih Asikin-Garmager, Sharon Han, Laura Bartlett","doi":"10.1093/jamia/ocae150","DOIUrl":"10.1093/jamia/ocae150","url":null,"abstract":"<p><strong>Objective: </strong>This case study describes how an All of Us engagement project returned value to community by strengthening high school students' capacity to serve as health advocates.</p><p><strong>Materials and methods: </strong>Project activities included health literacy education and research projects on the influence of environmental, societal, and lifestyle factors on community health disparities. The research project involved use of the Photovoice method and All of Us data. At project's end, students presented their research to the community.</p><p><strong>Results: </strong>The project's success was measured by students' participation in the research poster session and comparison of pre- and post-project scores from the Health Literacy Assessment Scale for Adolescent. Data analysis suggests the project succeeded in meeting its goal of increasing students' health literacy.</p><p><strong>Discussion and conclusion: </strong>Through education and research activities, students learned about community health issues and the importance of participation in medical research programs, like All of Us, to address issues.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"3001-3007"},"PeriodicalIF":4.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141421683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fair prediction of 2-year stroke risk in patients with atrial fibrillation.","authors":"Jifan Gao, Philip Mar, Zheng-Zheng Tang, Guanhua Chen","doi":"10.1093/jamia/ocae170","DOIUrl":"10.1093/jamia/ocae170","url":null,"abstract":"<p><strong>Objective: </strong>This study aims to develop machine learning models that provide both accurate and equitable predictions of 2-year stroke risk for patients with atrial fibrillation across diverse racial groups.</p><p><strong>Materials and methods: </strong>Our study utilized structured electronic health records (EHR) data from the All of Us Research Program. Machine learning models (LightGBM) were utilized to capture the relations between stroke risks and the predictors used by the widely recognized CHADS2 and CHA2DS2-VASc scores. We mitigated the racial disparity by creating a representative tuning set, customizing tuning criteria, and setting binary thresholds separately for subgroups. We constructed a hold-out test set that not only supports temporal validation but also includes a larger proportion of Black/African Americans for fairness validation.</p><p><strong>Results: </strong>Compared to the original CHADS2 and CHA2DS2-VASc scores, significant improvements were achieved by modeling their predictors using machine learning models (Area Under the Receiver Operating Characteristic curve from near 0.70 to above 0.80). Furthermore, applying our disparity mitigation strategies can effectively enhance model fairness compared to the conventional cross-validation approach.</p><p><strong>Discussion: </strong>Modeling CHADS2 and CHA2DS2-VASc risk factors with LightGBM and our disparity mitigation strategies achieved decent discriminative performance and excellent fairness performance. In addition, this approach can provide a complete interpretation of each predictor. These highlight its potential utility in clinical practice.</p><p><strong>Conclusions: </strong>Our research presents a practical example of addressing clinical challenges through the All of Us Research Program data. The disparity mitigation framework we proposed is adaptable across various models and data modalities, demonstrating broad potential in clinical informatics.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"2820-2828"},"PeriodicalIF":4.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11631105/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141499494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cigarette smoking, e-cigarette use, and sociodemographic correlates of mental health and tobacco-related disease risk in the All of Us Research Program.","authors":"Thomas R Kirchner, Danning Tian, Jian Li, Pranjal Srivastava, Yihao Zheng","doi":"10.1093/jamia/ocae237","DOIUrl":"10.1093/jamia/ocae237","url":null,"abstract":"<p><strong>Significance: </strong>Research on the conditions under which electronic cigarette (EC) use produces a net reduction in the population harm attributable to combusted cigarette (CC) use requires the triangulation of information from cohort(s) of smokers, non-smokers, EC users, and dual-users of all varieties.</p><p><strong>Materials and methods: </strong>This project utilizes data from the All of Us Research Program to contrast a panel of wellness and disease-risk indicators across a range of self-reported tobacco-use profiles, including smokers, current, and former EC users. This article focuses on the tobacco use history and current tobacco use status among All of Us participants enrolled between May 2017 and February 2023 (Registered Controlled Tier Curated Data Repository [CDR] v7).</p><p><strong>Results: </strong>The present analytic sample included an unweighted total of N = 412 211 individuals with information on ever-use of both CC and EC. Among them, 155 901 individuals have a history of CC use, with 65 206 identified as current smokers. EC usage is reported by 64 002 individuals, with 16 619 being current users. Model predicted analyses identified distinct patterns in CC and EC usage across demographic and socioeconomic variables, with younger ages favoring ECs.</p><p><strong>Discussion: </strong>Age was observed to significantly affect EC usage, and gender differences reveal that males were significantly more likely to use CC and/or EC than females or African Americans of any gender. Higher educational achievement and income were associated with lower use of both CC and EC, while lower levels of mental health were observed to increase the likelihood of using CC and EC products.</p><p><strong>Conclusion: </strong>Findings suggest the potential for the All of Us Research Program for investigation of causal factors driving both behavioral use transitions and cessation outcomes.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"2829-2836"},"PeriodicalIF":4.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11631103/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142134296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Health disparities in the risk of severe acidosis: real-world evidence from the All of Us cohort.","authors":"Allison E Gatz, Chenxi Xiong, Yao Chen, Shihui Jiang, Chi Mai Nguyen, Qianqian Song, Xiaochun Li, Pengyue Zhang, Michael T Eadon, Jing Su","doi":"10.1093/jamia/ocae256","DOIUrl":"10.1093/jamia/ocae256","url":null,"abstract":"<p><strong>Objective: </strong>To assess the health disparities across social determinants of health (SDoH) domains for the risk of severe acidosis independent of demographical and clinical factors.</p><p><strong>Materials and methods: </strong>A retrospective case-control study (n = 13 310, 1:4 matching) is performed using electronic health records (EHRs), SDoH surveys, and genomics data from the All of Us participants. The propensity score matching controls confounding effects due to EHR data availability. Conditional logistic regressions are used to estimate odds ratios describing associations between SDoHs and the risk of acidosis events, adjusted for demographic features, and clinical conditions.</p><p><strong>Results: </strong>Those with employer-provided insurance and those with Medicaid plans show dramatically different risks [adjusted odds ratio (AOR): 0.761 vs 1.41]. Low-income groups demonstrate higher risk (household income less than $25k, AOR: 1.3-1.57) than high-income groups ($100-$200k, AOR: 0.597-0.867). Other high-risk factors include impaired mobility (AOR: 1.32), unemployment (AOR: 1.32), renters (AOR: 1.41), other non-house-owners (AOR: 1.7), and house instability (AOR: 1.25). Education was negatively associated with acidosis risk.</p><p><strong>Discussion: </strong>Our work provides real-world evidence of the comprehensive health disparities due to socioeconomic and behavioral contributors in a cohort enriched in minority groups or underrepresented populations.</p><p><strong>Conclusions: </strong>SDoHs are strongly associated with systematic health disparities in the risk of severe metabolic acidosis. Types of health insurance, household income levels, housing status and stability, employment status, educational level, and mobility disability play significant roles after being adjusted for demographic features and clinical conditions. Comprehensive solutions are needed to improve equity in healthcare and reduce the risk of severe acidosis.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"2932-2939"},"PeriodicalIF":4.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11631078/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142479225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Engagement with health research summaries via digital communication to All of Us participants.","authors":"Janna Ter Meer, Royan Kamyar, Christina Orlovsky, Ting-Yang Hung, Tamara Benrey, Ethan Dinh-Luong, Giorgio Quer, Julia Moore Vogel","doi":"10.1093/jamia/ocae185","DOIUrl":"10.1093/jamia/ocae185","url":null,"abstract":"<p><strong>Objective: </strong>Summaries of health research can be a complementary way to return value to participants. We assess how research participants engage with summaries via email communication and how this can be improved.</p><p><strong>Materials and methods: </strong>We look at correlations between demographic subgroups and engagement in a longitudinal dataset of 305 626 participants (77% are classified as underrepresented in biomedical research) from the All of Us Research Program. We compare this against engagement with other program communications and use impact evaluations (N = 421 510) to measure the effect of tailoring communication by (1) eliciting content preferences, (2) Spanish focused content, (3) informational videos, and (4) article content in the email subject line.</p><p><strong>Results: </strong>Between March 2020 and October 2021, research summaries reached 67% of enrolled participants, outperforming other program communication (60%) and return of results (31%), which have a high uptake rate but have been extended to a subset of eligible participants. While all demographic subgroups engage with research summaries, participants with higher income, educational attainment, White, and older than 45 years open and click content most often. Surfacing article content in the email subject line and Spanish focused content had negative effects on engagement. Video and social media content and eliciting preferences led to a small directional increase in clicks.</p><p><strong>Discussion: </strong>Further individualization of tailoring efforts may be needed to drive larger engagement effects (eg, delivering multiple articles in line with stated preferences, expanding preference options). Our findings are likely a conservative representation of engagement effects, given the coarseness of our click rate measure.</p><p><strong>Conclusions: </strong>Health research summaries show promise as a way to return value to research participants, especially if individual-level results cannot be returned. Personalization of communication requires testing to determine whether efforts are having the expected effect.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"2908-2915"},"PeriodicalIF":4.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11631099/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141762142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Returning value to the community through the All of Us Research Program Data Sandbox model.","authors":"Elizabeth Cohn, Frida Esther Kleiman, Shayaa Muhammad, S Scott Jones, Nakisa Pourkey, Louise Bier","doi":"10.1093/jamia/ocae174","DOIUrl":"10.1093/jamia/ocae174","url":null,"abstract":"<p><strong>Objective: </strong>The All of Us Research Program aims to return value to participants by developing research capacity in communities. We describe a novel set of introductory exercises (Data Sandboxes) and specialized trainings to orient researchers to the Researcher Workbench to foster health equity research.</p><p><strong>Materials and methods: </strong>We developed a tailored training to familiarize researchers with the All of Us Research Program: (1) orientation, (2) tailored \"data treasure hunt\" using the Public Data Browser, and (3) overview of the analyses tools and platform.</p><p><strong>Results: </strong>Participants' pre- and post-knowledge of the contents and structure of the All of Us dataset scores increased significantly after training. These trainings effectively engaged researchers in exploring this rich dataset.</p><p><strong>Conclusion: </strong>We describe ways of orienting and familiarizing a wide variety of researchers with the All of Us Research Program dataset, sparking their interest, and \"jump-starting\" their research.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"2980-2984"},"PeriodicalIF":4.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11631172/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141793942","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A model for supporting biomedical and public health researcher use of publicly available All of Us data at Historically Black Colleges and Universities.","authors":"Brian Southwell, Sula Hood, Javan Carter, Courtney Richardson, Sheri Cates, Hadyatoullaye Sow, MaryBeth Branigan, Trey-Rashad Hawkins, Katie Atkinson, Jennifer Uhrig, Megan Lewis","doi":"10.1093/jamia/ocae099","DOIUrl":"10.1093/jamia/ocae099","url":null,"abstract":"<p><strong>Purpose: </strong>The aim of this study was to describe opportunities and challenges associated with the development and implementation of a program for supporting researchers underrepresented in biomedical research.</p><p><strong>Approach: </strong>We describe a case study of the All of Us Researcher Academy supported by the National Institutes of Health (NIH), including feedback from participants, instructors, and coaches.</p><p><strong>Findings: </strong>Lessons include the importance of inviting role models into learning networks, establishing and maintaining trusted relationships, and making coaches available for technical questions from researcher participants.</p><p><strong>Originality: </strong>Although research has focused on learning outcomes in science, technology, engineering, and mathematics at Minority Serving Institutions in the United States, literature tends to lack models for initiatives to improve everyday research experiences of faculty and researchers at such institutions or to encourage researcher use of public-use data such as that available through NIH's All of Us Research Program. The All of Us Researcher Academy offers a model that addresses these needs.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"2989-2993"},"PeriodicalIF":4.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11631136/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140909048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Disparities in ABO blood type determination across diverse ancestries: a systematic review and validation in the All of Us Research Program.","authors":"Kiana L Martinez, Andrew Klein, Jennifer R Martin, Chinwuwanuju U Sampson, Jason B Giles, Madison L Beck, Krupa Bhakta, Gino Quatraro, Juvie Farol, Jason H Karnes","doi":"10.1093/jamia/ocae161","DOIUrl":"10.1093/jamia/ocae161","url":null,"abstract":"<p><strong>Objectives: </strong>ABO blood types have widespread clinical use and robust associations with disease. The purpose of this study is to evaluate the portability and suitability of tag single-nucleotide polymorphisms (tSNPs) used to determine ABO alleles and blood types across diverse populations in published literature.</p><p><strong>Materials and methods: </strong>Bibliographic databases were searched for studies using tSNPs to determine ABO alleles. We calculated linkage between tSNPs and functional variants across inferred continental ancestry groups from 1000 Genomes. We compared r2 across ancestry and assessed real-world consequences by comparing tSNP-derived blood types to serology in a diverse population from the All of Us Research Program.</p><p><strong>Results: </strong>Linkage between functional variants and O allele tSNPs was significantly lower in African (median r2 = 0.443) compared to East Asian (r2 = 0.946, P = 1.1 × 10-5) and European (r2 = 0.869, P = .023) populations. In All of Us, discordance between tSNP-derived blood types and serology was high across all SNPs in African ancestry individuals and linkage was strongly correlated with discordance across all ancestries (ρ = -0.90, P = 3.08 × 10-23).</p><p><strong>Discussion: </strong>Many studies determine ABO blood types using tSNPs. However, tSNPs with low linkage disequilibrium promote misinference of ABO blood types, particularly in diverse populations. We observe common use of inappropriate tSNPs to determine ABO blood type, particularly for O alleles and with some tSNPs mistyping up to 58% of individuals.</p><p><strong>Conclusion: </strong>Our results highlight the lack of transferability of tSNPs across ancestries and potential exacerbation of disparities in genomic research for underrepresented populations. This is especially relevant as more diverse cohorts are made publicly available.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"3022-3031"},"PeriodicalIF":4.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11631141/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141452052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pregnancy episodes in All of Us: harnessing multi-source data for pregnancy-related research.","authors":"Louisa H Smith, Wanjiang Wang, Brianna Keefe-Oates","doi":"10.1093/jamia/ocae195","DOIUrl":"10.1093/jamia/ocae195","url":null,"abstract":"<p><strong>Objectives: </strong>The National Institutes of Health's All of Us Research Program addresses gaps in biomedical research by collecting health data from diverse populations. Pregnant individuals have historically been underrepresented in biomedical research, and pregnancy-related research is often limited by data availability, sample size, and inadequate representation of the diversity of pregnant people. All of Us integrates a wealth of health-related data, providing a unique opportunity to conduct comprehensive pregnancy-related research. We aimed to identify pregnancy episodes with high-quality electronic health record (EHR) data in All of Us Research Program data and evaluate the program's utility for pregnancy-related research.</p><p><strong>Materials and methods: </strong>We used a previously published algorithm to identify pregnancy episodes in All of Us EHR data. We described these pregnancies, validated them with All of Us survey data, and compared them to national statistics.</p><p><strong>Results: </strong>Our study identified 18 970 pregnancy episodes from 14 234 participants; other possible pregnancy episodes had low-quality or insufficient data. Validation against people who reported a current pregnancy on an All of Us survey found low false positive and negative rates. Demographics were similar in some respects to national data; however, Asian-Americans were underrepresented, and older, highly educated pregnant people were overrepresented.</p><p><strong>Discussion: </strong>Our approach demonstrates the capacity of All of Us to support pregnancy research and reveals the diversity of the pregnancy cohort. However, we noted an underrepresentation among some demographics. Other limitations include measurement error in gestational age and limited data on non-live births.</p><p><strong>Conclusion: </strong>The wide variety of data in the All of Us program, encompassing EHR, survey, genomic, and fitness tracker data, offers a valuable resource for studying pregnancy, yet care must be taken to avoid biases.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"2789-2799"},"PeriodicalIF":4.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11631125/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141753239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting mortality in hospitalized influenza patients: integration of deep learning-based chest X-ray severity score (FluDeep-XR) and clinical variables.","authors":"Meng-Han Tsai, Sung-Chu Ko, Amy Huaishiuan Huang, Lorenzo Porta, Cecilia Ferretti, Clarissa Longhi, Wan-Ting Hsu, Yung-Han Chang, Jo-Ching Hsiung, Chin-Hua Su, Filippo Galbiati, Chien-Chang Lee","doi":"10.1093/jamia/ocae286","DOIUrl":"https://doi.org/10.1093/jamia/ocae286","url":null,"abstract":"<p><strong>Objectives: </strong>To pioneer the first artificial intelligence system integrating radiological and objective clinical data, simulating the clinical reasoning process, for the early prediction of high-risk influenza patients.</p><p><strong>Materials and methods: </strong>Our system was developed using a cohort from National Taiwan University Hospital in Taiwan, with external validation data from ASST Grande Ospedale Metropolitano Niguarda in Italy. Convolutional neural networks pretrained on ImageNet were regressively trained using a 5-point scale to develop the influenza chest X-ray (CXR) severity scoring model, FluDeep-XR. Early, late, and joint fusion structures, incorporating varying weights of CXR severity with clinical data, were designed to predict 30-day mortality and compared with models using only CXR or clinical data. The best-performing model was designated as FluDeep. The explainability of FluDeep-XR and FluDeep was illustrated through activation maps and SHapley Additive exPlanations (SHAP).</p><p><strong>Results: </strong>The Xception-based model, FluDeep-XR, achieved a mean square error of 0.738 in the external validation dataset. The Random Forest-based late fusion model, FluDeep, outperformed all the other models, achieving an area under the receiver operating curve of 0.818 and a sensitivity of 0.706 in the external dataset. Activation maps highlighted clear lung fields. Shapley additive explanations identified age, C-reactive protein, hematocrit, heart rate, and respiratory rate as the top 5 important clinical features.</p><p><strong>Discussion: </strong>The integration of medical imaging with objective clinical data outperformed single-modality models to predict 30-day mortality in influenza patients. We ensured the explainability of our models aligned with clinical knowledge and validated its applicability across foreign institutions.</p><p><strong>Conclusion: </strong>FluDeep highlights the potential of combining radiological and clinical information in late fusion design, enhancing diagnostic accuracy and offering an explainable, and generalizable decision support system.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142689371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}