{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,22]],"date-time":"2026-05-22T23:06:11Z","timestamp":1779491171374,"version":"3.53.1"},"reference-count":47,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T00:00:00Z","timestamp":1773100800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"name":"SNUH Lee Kun-hee Child Cancer & Rare Disease Project"},{"name":"Republic of Korea","award":["22A-000-0000"],"award-info":[{"award-number":["22A-000-0000"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,6,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Traditional electronic health record (EHR) foundation models fail to process unseen medical codes, limiting generalizability across institutions with different vocabularies. To address this problem, we introduce medical concept representation (MedRep), standardized medical concept representations for EHR foundation models, enabling recognition of semantically similar concepts regardless of their specific IDs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>We utilized Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) vocabulary covering 7.5 million concepts from 66 medical vocabularies. MedRep integrates large language model\u2013generated concept descriptions and OMOP graph ontology using graph contrastive learning with knowledge distillation. We evaluated MedRep-based models on MIMIC-IV (internal validation) and EHRSHOT (external validation) across 9 prediction tasks including clinical outcomes, phenotypes, and in-hospital events.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>MedRep consistently outperformed baseline models, particularly in external validation with average improvements of 0.088 in area under the receiver operating characteristic curve and 0.208 in area under the precision-recall curve. Qualitative analysis demonstrated that MedRep-based models identified more clinically relevant concepts when making decisions than the baseline models. Performance improvements remained stable across diverse EHR foundation model architectures, including BEHRT, Med-BERT, and CDM-BERT.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>MedRep improves the generalizability of EHR foundation models by encouraging similar concepts to have similar representations. EHR foundation models developed at different institutions could cooperate through MedRep, merging knowledge from multiple hospital datasets. In addition, our approach could reduce healthcare disparities by enabling smaller institutions to benefit from models trained on larger datasets.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>MedRep improves EHR foundation model performance, interpretability, and generalizability, serving as a standard baseline representation for EHR foundation models adopting OMOP CDM.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/jamia\/ocag032","type":"journal-article","created":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T12:47:49Z","timestamp":1772023669000},"page":"1089-1099","source":"Crossref","is-referenced-by-count":0,"title":["MedRep: medical concept representations for general electronic health record foundation models"],"prefix":"10.1093","volume":"33","author":[{"given":"Junmo","family":"Kim","sequence":"first","affiliation":[{"name":"Interdisciplinary Program in Bioengineering, Seoul National University , Seoul 08826,","place":["Republic of Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Namkyeong","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Industrial and Systems Engineering, KAIST , Daejeon 34141,","place":["Republic of Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiwon","family":"Kim","sequence":"additional","affiliation":[{"name":"Interdisciplinary Program of Medical Informatics, Seoul National University , Seoul 03080,","place":["Republic of Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4586-5062","authenticated-orcid":false,"given":"Kwangsoo","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Transdisciplinary Medicine, Seoul National University Hospital , Seoul 03080,","place":["Republic of Korea"]},{"name":"Center for Data Science, Healthcare AI Research Institute, Seoul National University Hospital , Seoul 03080,","place":["Republic of Korea"]},{"name":"Department of Medicine, Seoul National University College of Medicine , Seoul 03080,","place":["Republic of Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,3,10]]},"reference":[{"key":"2026052218565051500_ocag032-B1","first-page":"13","article-title":"Hospitals\u2019 use of electronic health records data, 2015\u20132017","volume":"46","author":"Parasrampuria","year":"2019","journal-title":"ONC Data Brief"},{"key":"2026052218565051500_ocag032-B2","doi-asserted-by":"publisher","first-page":"e24813","DOI":"10.2196\/24813","article-title":"Adoption of electronic health records (EHRs) in China during the past 10 years: consecutive survey data analysis and comparison of Sino-American challenges and experiences","volume":"23","author":"Liang","year":"2021","journal-title":"J Med Internet Res."},{"key":"2026052218565051500_ocag032-B3","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1038\/s41746-023-00875-y","article-title":"Development and external validation of a pretrained deep learning model for the prediction of non-accidental trauma","volume":"6","author":"Huang","year":"2023","journal-title":"NPJ Digit Med."},{"key":"2026052218565051500_ocag032-B4","doi-asserted-by":"publisher","first-page":"3024","DOI":"10.1109\/TKDE.2023.3329025","article-title":"Enhancing drug recommendations via heterogeneous graph representation learning in EHR networks","volume":"36","author":"Zhang","year":"2024","journal-title":"IEEE Trans Knowl Data Eng."},{"key":"2026052218565051500_ocag032-B5","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1038\/s41746-024-01215-4","article-title":"Deep learning-based prediction of Clostridioides difficile infection caused by antibiotics using longitudinal electronic health records","volume":"7","author":"Kim","year":"2024","journal-title":"NPJ Digit Med."},{"key":"2026052218565051500_ocag032-B6","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":"2026052218565051500_ocag032-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":"2026052218565051500_ocag032-B8","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":"2026052218565051500_ocag032-B9","doi-asserted-by":"publisher","first-page":"256","DOI":"10.1038\/s41746-024-01235-0","article-title":"Zero shot health trajectory prediction using transformer","volume":"7","author":"Renc","year":"2024","journal-title":"NPJ Digit Med."},{"key":"2026052218565051500_ocag032-B10","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1038\/s41746-024-01166-w","article-title":"A multi-center study on the adaptability of a shared foundation model for electronic health records","volume":"7","author":"Guo","year":"2024","journal-title":"NPJ Digit Med."},{"key":"2026052218565051500_ocag032-B11","first-page":"279","article-title":"SNOMED-CT: the advanced terminology and coding system for eHealth","volume":"121","author":"Donnelly","year":"2006","journal-title":"Stud Health Technol Inform."},{"key":"2026052218565051500_ocag032-B12","doi-asserted-by":"publisher","first-page":"1130","DOI":"10.1097\/01.mlr.0000182534.19832.83","article-title":"Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data","volume":"43","author":"Quan","year":"2005","journal-title":"Med Care."},{"key":"2026052218565051500_ocag032-B13","doi-asserted-by":"publisher","first-page":"600","DOI":"10.7326\/0003-4819-153-9-201011020-00010","article-title":"Advancing the science for active surveillance: rationale and design for the Observational Medical Outcomes Partnership","volume":"153","author":"Stang","year":"2010","journal-title":"Ann Intern Med."},{"key":"2026052218565051500_ocag032-B14","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1109\/MITP.2005.122","article-title":"RxNorm: prescription for electronic drug information exchange","volume":"7","author":"Liu","year":"2005","journal-title":"IT Prof."},{"key":"2026052218565051500_ocag032-B15","doi-asserted-by":"publisher","first-page":"624","DOI":"10.1373\/49.4.624","article-title":"LOINC, a universal standard for identifying laboratory observations: a 5-year update","volume":"49","author":"McDonald","year":"2003","journal-title":"Clin Chem."},{"key":"2026052218565051500_ocag032-B16","doi-asserted-by":"publisher","first-page":"232","DOI":"10.1038\/s43856-025-00914-7","article-title":"Pretrained patient trajectories for adverse drug event prediction using common data model-based electronic health records","volume":"5","author":"Kim","year":"2025","journal-title":"Commun Med (Lond)."},{"key":"2026052218565051500_ocag032-B17","author":"Schick","year":"2021"},{"key":"2026052218565051500_ocag032-B18","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1038\/s41597-022-01899-x","article-title":"MIMIC-IV, a freely accessible electronic health record dataset","volume":"10","author":"Johnson","year":"2023","journal-title":"Sci Data."},{"key":"2026052218565051500_ocag032-B19","author":"Wornow","year":"2023"},{"key":"2026052218565051500_ocag032-B20","doi-asserted-by":"publisher","first-page":"3767","DOI":"10.1038\/s41598-023-30820-8","article-title":"EHR foundation models improve robustness in the presence of temporal distribution shift","volume":"13","author":"Guo","year":"2023","journal-title":"Sci Rep."},{"key":"2026052218565051500_ocag032-B21","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1038\/s41597-019-0103-9","article-title":"Multitask learning and benchmarking with clinical time series data","volume":"6","author":"Harutyunyan","year":"2019","journal-title":"Sci Data."},{"key":"2026052218565051500_ocag032-B22","doi-asserted-by":"publisher","first-page":"e0248636","DOI":"10.1371\/journal.pone.0248636","article-title":"Prediction of risk of acquiring urinary tract infection during hospital stay based on machine-learning: a retrospective cohort study","volume":"16","author":"M\u00f8ller","year":"2021","journal-title":"PLoS One."},{"key":"2026052218565051500_ocag032-B23","doi-asserted-by":"publisher","first-page":"e22550","DOI":"10.2196\/22550","article-title":"Deep learning with electronic health records for short-term fracture risk identification: crystal bone algorithm development and validation","volume":"22","author":"Almog","year":"2020","journal-title":"J Med Internet Res."},{"key":"2026052218565051500_ocag032-B24","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1097\/CCM.0000000000002936","article-title":"An interpretable machine learning model for accurate prediction of sepsis in the ICU","volume":"46","author":"Nemati","year":"2018","journal-title":"Crit Care Med."},{"key":"2026052218565051500_ocag032-B25","doi-asserted-by":"publisher","first-page":"938801","DOI":"10.3389\/fpubh.2022.938801","article-title":"Machine learning-assisted prediction of pneumonia based on non-invasive measures","volume":"10","author":"Effah","year":"2022","journal-title":"Front Public Health."},{"key":"2026052218565051500_ocag032-B26","doi-asserted-by":"publisher","first-page":"252","DOI":"10.1186\/s12911-020-01268-x","article-title":"Prediction of incident myocardial infarction using machine learning applied to harmonized electronic health record data","volume":"20","author":"Mandair","year":"2020","journal-title":"BMC Med Inform Decis Mak."},{"key":"2026052218565051500_ocag032-B27","first-page":"4171","author":"Devlin"},{"key":"2026052218565051500_ocag032-B28","author":"He","year":"2021"},{"key":"2026052218565051500_ocag032-B29","doi-asserted-by":"publisher","author":"Zhu","year":"2020","DOI":"10.48550\/arXiv.2006.04131"},{"key":"2026052218565051500_ocag032-B30","first-page":"67533","article-title":"Mitigating the popularity bias of graph collaborative filtering: a dimensional collapse perspective","volume":"36","author":"Zhang","year":"2023","journal-title":"Adv Neural Inf Process Syst."},{"key":"2026052218565051500_ocag032-B31","doi-asserted-by":"publisher","first-page":"2935","DOI":"10.1109\/TPAMI.2017.2773081","article-title":"Learning without forgetting","volume":"40","author":"Li","year":"2018","journal-title":"IEEE Trans Pattern Anal Mach Intell."},{"key":"2026052218565051500_ocag032-B32","doi-asserted-by":"publisher","author":"Goodfellow","year":"2013","DOI":"10.48550\/arXiv.1312.6211"},{"key":"2026052218565051500_ocag032-B33","first-page":"109","author":"McCloskey","year":"1989"},{"key":"2026052218565051500_ocag032-B34","first-page":"146","article-title":"I-divergence geometry of probability distributions and minimization problems","author":"Csisz\u00e1r","year":"1975","journal-title":"The Ann Probab"},{"key":"2026052218565051500_ocag032-B35","author":"Su","year":"2025"},{"key":"2026052218565051500_ocag032-B36","doi-asserted-by":"publisher","author":"Sellergren","year":"2025","DOI":"10.48550\/arXiv.2507.05201"},{"key":"2026052218565051500_ocag032-B37","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1007\/s10916-022-01865-5","article-title":"Preserving patient privacy during computation over shared electronic health record data","volume":"46","author":"d\u2019Aliberti","year":"2022","journal-title":"J Med Syst."},{"key":"2026052218565051500_ocag032-B38","author":"Gliklich","year":"2019"},{"key":"2026052218565051500_ocag032-B39","first-page":"95","author":"Reinecke","year":"2021"},{"key":"2026052218565051500_ocag032-B40","doi-asserted-by":"publisher","first-page":"238","DOI":"10.1186\/s12874-021-01434-3","article-title":"Standardizing registry data to the OMOP Common Data Model: experience from three pulmonary hypertension databases","volume":"21","author":"Biedermann","year":"2021","journal-title":"BMC Med Res Methodol."},{"key":"2026052218565051500_ocag032-B41","doi-asserted-by":"publisher","first-page":"949","DOI":"10.1001\/jama.1967.03130120057014","article-title":"Subnormal levels of glucose in urine: a sign of urinary tract infection","volume":"201","author":"Scherst\u00e9n","year":"1967","journal-title":"Jama."},{"key":"2026052218565051500_ocag032-B42","first-page":"292","article-title":"Fungus infections of the urinary tract","volume":"30","author":"Guze","year":"1958","journal-title":"Yale J Biol Med."},{"key":"2026052218565051500_ocag032-B43","doi-asserted-by":"publisher","first-page":"735","DOI":"10.1016\/S0891-5520","article-title":"Bacterial urinary tract infections in diabetes","volume":"11","author":"Patterson","year":"1997","journal-title":"Infect Dis Clin North Am."},{"key":"2026052218565051500_ocag032-B44","doi-asserted-by":"publisher","first-page":"ITC66","DOI":"10.7326\/AITC201711070","article-title":"Acute kidney injury","volume":"167","author":"Levey","year":"2017","journal-title":"Ann Intern Med."},{"key":"2026052218565051500_ocag032-B45","doi-asserted-by":"publisher","author":"Kaplan","year":"2020","DOI":"10.48550\/arXiv.2001.08361"},{"key":"2026052218565051500_ocag032-B46","first-page":"249","author":"Liu"},{"key":"2026052218565051500_ocag032-B47","first-page":"14510","author":"Wan"}],"container-title":["Journal of the American Medical Informatics Association"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/jamia\/article-pdf\/33\/6\/1089\/67289124\/ocag032.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/jamia\/article-pdf\/33\/6\/1089\/67289124\/ocag032.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,22]],"date-time":"2026-05-22T22:56:58Z","timestamp":1779490618000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/jamia\/article\/33\/6\/1089\/8513071"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,10]]},"references-count":47,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2026,3,10]]},"published-print":{"date-parts":[[2026,6,1]]}},"URL":"https:\/\/doi.org\/10.1093\/jamia\/ocag032","relation":{},"ISSN":["1067-5027","1527-974X"],"issn-type":[{"value":"1067-5027","type":"print"},{"value":"1527-974X","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2026,6]]},"published":{"date-parts":[[2026,3,10]]}}}