{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T14:41:58Z","timestamp":1780497718623,"version":"3.54.1"},"reference-count":48,"publisher":"Oxford University Press (OUP)","issue":"11","license":[{"start":{"date-parts":[[2024,6,25]],"date-time":"2024-06-25T00:00:00Z","timestamp":1719273600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000092","name":"National Library of Medicine","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000092","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Institute of Health","award":["R01LM013771"],"award-info":[{"award-number":["R01LM013771"]}]},{"DOI":"10.13039\/100000054","name":"National Cancer Institute","doi-asserted-by":"publisher","award":["P30CA 082709"],"award-info":[{"award-number":["P30CA 082709"]}],"id":[{"id":"10.13039\/100000054","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000057","name":"National Institute of General Medical Sciences","doi-asserted-by":"publisher","award":["R35GM151089"],"award-info":[{"award-number":["R35GM151089"]}],"id":[{"id":"10.13039\/100000057","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Indiana University Precision Health Initiative"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,11,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Objective<\/jats:title>\n                  <jats:p>Our objective is to develop and validate TrajVis, an interactive tool that assists clinicians in using artificial intelligence (AI) models to leverage patients\u2019 longitudinal electronic medical records (EMRs) for personalized precision management of chronic disease progression.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Materials and Methods<\/jats:title>\n                  <jats:p>We first perform requirement analysis with clinicians and data scientists to determine the visual analytics tasks of the TrajVis system as well as its design and functionalities. A graph AI model for chronic kidney disease (CKD) trajectory inference named DisEase PrOgression Trajectory (DEPOT) is used for system development and demonstration. TrajVis is implemented as a full-stack web application with synthetic EMR data derived from the Atrium Health Wake Forest Baptist Translational Data Warehouse and the Indiana Network for Patient Care research database. A case study with a nephrologist and a user experience survey of clinicians and data scientists are conducted to evaluate the TrajVis system.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>The TrajVis clinical information system is composed of 4 panels: the Patient View for demographic and clinical information, the Trajectory View to visualize the DEPOT-derived CKD trajectories in latent space, the Clinical Indicator View to elucidate longitudinal patterns of clinical features and interpret DEPOT predictions, and the Analysis View to demonstrate personal CKD progression trajectories. System evaluations suggest that TrajVis supports clinicians in summarizing clinical data, identifying individualized risk predictors, and visualizing patient disease progression trajectories, overcoming the barriers of AI implementation in healthcare.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Discussion<\/jats:title>\n                  <jats:p>The TrajVis system provides a novel visualization solution which is complimentary to other risk estimators such as the Kidney Failure Risk Equations.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusion<\/jats:title>\n                  <jats:p>TrajVis bridges the gap between the fast-growing AI\/ML modeling and the clinical use of such models for personalized and precision management of chronic diseases.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/jamia\/ocae158","type":"journal-article","created":{"date-parts":[[2024,6,25]],"date-time":"2024-06-25T16:02:01Z","timestamp":1719331321000},"page":"2474-2485","source":"Crossref","is-referenced-by-count":17,"title":["TrajVis: a visual clinical decision support system to translate artificial intelligence trajectory models in the precision management of chronic kidney disease"],"prefix":"10.1093","volume":"31","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8317-2873","authenticated-orcid":false,"given":"Zuotian","family":"Li","sequence":"first","affiliation":[{"name":"Department of Biostatistics and Health Data Science, Indiana University School of Medicine , Indianapolis, IN 46202,","place":["United States"]},{"name":"Department of Computer Graphics Technology, Purdue University , West Lafayette, IN 47907,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiang","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Biostatistics and Health Data Science, Indiana University School of Medicine , Indianapolis, IN 46202,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziyang","family":"Tang","sequence":"additional","affiliation":[{"name":"Department of Computer and Information Technology, Purdue University , West Lafayette, IN 47907,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nanxin","family":"Jin","sequence":"additional","affiliation":[{"name":"Department of Biostatistics and Health Data Science, Indiana University School of Medicine , Indianapolis, IN 46202,","place":["United States"]},{"name":"Department of Computer and Information Technology, Purdue University , West Lafayette, IN 47907,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pengyue","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Biostatistics and Health Data Science, Indiana University School of Medicine , Indianapolis, IN 46202,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3066-2876","authenticated-orcid":false,"given":"Michael T","family":"Eadon","sequence":"additional","affiliation":[{"name":"Department of Medicine, Indiana University School of Medicine , Indianapolis, IN 46202,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4455-5302","authenticated-orcid":false,"given":"Qianqian","family":"Song","sequence":"additional","affiliation":[{"name":"Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida , Gainesville, FL 32610,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yingjie V","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Computer Graphics Technology, Purdue University , West Lafayette, IN 47907,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4917-6173","authenticated-orcid":false,"given":"Jing","family":"Su","sequence":"additional","affiliation":[{"name":"Department of Biostatistics and Health Data Science, Indiana University School of Medicine , Indianapolis, IN 46202,","place":["United States"]},{"name":"Gerontology and Geriatric Medicine, Wake Forest School of Medicine , Winston-Salem, NC 27101,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2024,6,25]]},"reference":[{"issue":"1","key":"2024102107533913000_ocae158-B1","first-page":"1","article-title":"Kidney disease: improving global outcomes (KDIGO) CKD work group. 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