{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T12:07:17Z","timestamp":1790770037235,"version":"4.1.0"},"reference-count":22,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T00:00:00Z","timestamp":1790726400000},"content-version":"vor","delay-in-days":21,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000050","name":"National Heart, Lung, and Blood Institute","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000050","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01 HL163049"],"award-info":[{"award-number":["R01 HL163049"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,9,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>Transformer-based models for clinical prediction using longitudinal electronic health record (EHR) data are often developed with large feature sets and long patient histories under the assumption that more data improves performance. However, high-dimensional inputs and long sequences increase computational burden, potentially limiting scalability. We evaluated how feature dimensionality and sequence length affect predictive performance, calibration, risk stratification, and computational efficiency in EHR prediction.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>Using longitudinal EHR data from adults with mild asthma in an integrated healthcare system, we evaluated input representation design for predicting acute asthma exacerbation. Feature dimensionality was reduced using Integrated Gradients attribution scores, univariate performance-based selection, and clinically guided selection strategies. Sequence length was varied using percentile-based truncation of patient histories. Performance was assessed using discrimination, calibration, high-risk classification, threshold-based event capture, and computational efficiency across regions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Models using fewer features achieved discrimination comparable to the 80-feature reference model, with area under the receiver operating characteristic curve values ranging from 0.843 to 0.864 vs 0.870 for the full model. Moderate sequence-length truncation reduced training time by more than 70% with minimal loss in discrimination. Although reduced-dimensional models showed attenuation of predicted risk at the upper tail, they identified similar high-risk populations and captured comparable proportions of asthma exacerbation events at clinically relevant thresholds.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Transformer-based prediction models maintained strong performance across reduced feature sets. While dimensionality reduction modestly affected calibration at the highest risk levels, moderate sequence-length reduction substantially reduced computational burden with limited change in overall discrimination. These findings highlight tradeoffs between input complexity, predictive performance, and computational efficiency.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/jamiaopen\/ooag193","type":"journal-article","created":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T18:21:57Z","timestamp":1789755717000},"source":"Crossref","is-referenced-by-count":0,"title":["Rethinking input complexity in transformer-based clinical prediction: implications for feature dimensionality and sequence length in longitudinal electronic health record data"],"prefix":"10.1093","volume":"9","author":[{"given":"Wansu","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Research and Evaluation, Kaiser Permanente Southern California , Pasadena, CA 91101,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Botao","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Research and Evaluation, Kaiser Permanente Southern California , Pasadena, CA 91101,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robert S","family":"Zeiger","sequence":"additional","affiliation":[{"name":"Department of Allergy, Kaiser Permanente Southern California , San Diego, CA 92111,","place":["United States"]},{"name":"Department of Clinical Science, Kaiser Permanente Bernard J. Tyson School of Medicine , Pasadena, CA 91101,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"William","family":"Crawford","sequence":"additional","affiliation":[{"name":"Department of Allergy, Kaiser Permanente Southern California , Harbor City, CA 90710,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Schatz","sequence":"additional","affiliation":[{"name":"Department of Allergy, Kaiser Permanente Southern California , San Diego, CA 92111,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eric","family":"Puttock","sequence":"additional","affiliation":[{"name":"Department of Research and Evaluation, Kaiser Permanente Southern California , Pasadena, CA 91101,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stanley","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Research and Evaluation, Kaiser Permanente Southern California , Pasadena, CA 91101,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matthew T","family":"Slaughter","sequence":"additional","affiliation":[{"name":"Kaiser Permanente Center for Health Research , Portland, OR 97227,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1565-0490","authenticated-orcid":false,"given":"Fagen","family":"Xie","sequence":"additional","affiliation":[{"name":"Department of Research and Evaluation, Kaiser Permanente Southern California , Pasadena, CA 91101,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,9,30]]},"reference":[{"key":"2026093007514886900_ooag193-B1","first-page":"301","article-title":"Doctor AI: predicting clinical events via recurrent neural networks","volume":"56","author":"Choi","year":"2016","journal-title":"JMLR Workshop Conf Proc"},{"key":"2026093007514886900_ooag193-B2","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1038\/s41746-018-0029-1","article-title":"Scalable and accurate deep learning with electronic health records","volume":"1","author":"Rajkomar","year":"2018","journal-title":"NPJ Digit Med"},{"key":"2026093007514886900_ooag193-B3","doi-asserted-by":"publisher","first-page":"1589","DOI":"10.1109\/JBHI.2017.2767063","article-title":"Deep EHR: a survey of recent advances in deep learning techniques for electronic health record (EHR) analysis","volume":"22","author":"Shickel","year":"2018","journal-title":"IEEE J Biomed Health Inform"},{"key":"2026093007514886900_ooag193-B4","doi-asserted-by":"publisher","first-page":"7155","DOI":"10.1038\/s41598-020-62922-y","article-title":"BEHRT: transformer for electronic health records","volume":"10","author":"Li","year":"2020","journal-title":"Sci Rep"},{"key":"2026093007514886900_ooag193-B5","doi-asserted-by":"publisher","first-page":"7857","DOI":"10.1038\/s41467-023-43715-z","article-title":"TransformEHR: transformer-based encoder-decoder generative model to enhance prediction of disease outcomes using electronic health records","volume":"14","author":"Yang","year":"2023","journal-title":"Nat Commun"},{"key":"2026093007514886900_ooag193-B6","doi-asserted-by":"publisher","first-page":"521","DOI":"10.1038\/s41746-025-01872-z","article-title":"Transformer patient embedding using electronic health records enables patient stratification and progression analysis","volume":"8","author":"Xian","year":"2025","journal-title":"NPJ Digit Med"},{"key":"2026093007514886900_ooag193-B7","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1038\/s41746-021-00455-y","article-title":"Med-BERT: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction","volume":"4","author":"Rasmy","year":"2021","journal-title":"NPJ Digit Med"},{"key":"2026093007514886900_ooag193-B8","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1186\/s12916-019-1426-2","article-title":"Key challenges for delivering clinical impact with artificial intelligence","volume":"17","author":"Kelly","year":"2019","journal-title":"BMC Med"},{"key":"2026093007514886900_ooag193-B9","doi-asserted-by":"publisher","first-page":"1337","DOI":"10.1038\/s41591-019-0548-6","article-title":"Do no harm: a roadmap for responsible machine learning for health care","volume":"25","author":"Wiens","year":"2019","journal-title":"Nat Med"},{"key":"2026093007514886900_ooag193-B10","doi-asserted-by":"publisher","first-page":"102900","DOI":"10.1016\/j.artmed.2024.102900","article-title":"Transformers and large language models in healthcare: a review","volume":"154","author":"Nerella","year":"2024","journal-title":"Artif Intell Med"},{"key":"2026093007514886900_ooag193-B11","first-page":"5998","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv Neural Inf Process Syst."},{"key":"2026093007514886900_ooag193-B12","doi-asserted-by":"crossref","DOI":"10.1007\/978-0-387-84858-7","volume-title":"The Elements of Statistical Learning: Data Mining, Inference, and Prediction","author":"Hastie","year":"2009","edition":"2nd ed."},{"key":"2026093007514886900_ooag193-B13","first-page":"1157","article-title":"An introduction to variable and feature selection","volume":"3","author":"Guyon","year":"2003","journal-title":"J Mach Learn Res"},{"key":"2026093007514886900_ooag193-B14","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1186\/s12916-019-1466-7","article-title":"Calibration: the Achilles heel of predictive analytics","volume":"17","author":"Van Calster","year":"2019","journal-title":"BMC Med"},{"key":"2026093007514886900_ooag193-B15","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1097\/EDE.0b013e3181c30fb2","article-title":"Assessing the performance of prediction models: a framework for traditional and novel measures","volume":"21","author":"Steyerberg","year":"2010","journal-title":"Epidemiology"},{"key":"2026093007514886900_ooag193-B16","doi-asserted-by":"publisher","first-page":"565","DOI":"10.1177\/0272989X06295361","article-title":"Decision curve analysis: a novel method for evaluating prediction models","volume":"26","author":"Vickers","year":"2006","journal-title":"Med Decis Making"},{"key":"2026093007514886900_ooag193-B17","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-026-02624-3","article-title":"Cohort vs case-control design for transformer-based prediction of asthma exacerbations in mild asthma","volume":"9","author":"Xie","year":"2026","journal-title":"NPJ Digit Med"},{"key":"2026093007514886900_ooag193-B18","doi-asserted-by":"publisher","first-page":"2705","DOI":"10.1016\/j.jaip.2024.05.034","article-title":"Risk factors for acute asthma exacerbations in adults with mild asthma","volume":"12","author":"Chen","year":"2024","journal-title":"J Allergy Clin Immunol Pract"},{"key":"2026093007514886900_ooag193-B19","first-page":"3319","article-title":"Axiomatic attribution for deep networks","volume":"70","author":"Sundararajan","year":"2017","journal-title":"Proc Mach Learn Res"},{"key":"2026093007514886900_ooag193-B20","author":"Ancona","year":"2018"},{"key":"2026093007514886900_ooag193-B21","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1016\/j.jclinepi.2015.04.005","article-title":"Prediction models need appropriate internal, internal-external, and external validation","volume":"69","author":"Steyerberg","year":"2016","journal-title":"J Clin Epidemiol"},{"key":"2026093007514886900_ooag193-B22","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1038\/s41591-018-0300-7","article-title":"High-performance medicine: the convergence of human and artificial intelligence","volume":"25","author":"Topol","year":"2019","journal-title":"Nat Med"}],"container-title":["JAMIA Open"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/jamiaopen\/article-pdf\/9\/5\/ooag193\/71518596\/ooag193.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/jamiaopen\/article-pdf\/9\/5\/ooag193\/71518596\/ooag193.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T11:51:56Z","timestamp":1790769116000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/jamiaopen\/article\/doi\/10.1093\/jamiaopen\/ooag193\/8853722"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,9]]},"references-count":22,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2026,9,9]]}},"URL":"https:\/\/doi.org\/10.1093\/jamiaopen\/ooag193","relation":{},"ISSN":["2574-2531"],"issn-type":[{"value":"2574-2531","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2026,10]]},"published":{"date-parts":[[2026,9,9]]},"article-number":"ooag193"}}