{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T04:30:57Z","timestamp":1790829057896,"version":"4.1.0"},"reference-count":85,"publisher":"Massachusetts Medical Society","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["NEJM AI"],"published-print":{"date-parts":[[2026,5,28]]},"DOI":"10.1056\/aira2501253","type":"journal-article","created":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T13:00:42Z","timestamp":1779973242000},"source":"Crossref","is-referenced-by-count":3,"title":["MEDS \u2014 An Emerging Data Standard and Ecosystem for Health AI Research"],"prefix":"10.1056","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6048-9707","authenticated-orcid":false,"given":"Matthew B. A.","family":"McDermott","sequence":"first","affiliation":[{"name":"Department of Biomedical Informatics, Columbia University, New York, NY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7166-5032","authenticated-orcid":false,"given":"Ethan","family":"Steinberg","sequence":"additional","affiliation":[{"name":"Center for Biomedical Informatics Research, Stanford University, Stanford, CA, USA"},{"name":"Prealize Health, Palo Alto, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9316-5768","authenticated-orcid":false,"given":"Jason A.","family":"Fries","sequence":"additional","affiliation":[{"name":"Center for Biomedical Informatics Research, Stanford University, Stanford, CA, USA"},{"name":"Department of Biomedical Data Science, Stanford University, Stanford, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2895-4872","authenticated-orcid":false,"given":"Robin P.","family":"van de Water","sequence":"additional","affiliation":[{"name":"Hasso Plattner Institute, University of Potsdam, Potsdam, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9308-8790","authenticated-orcid":false,"given":"Chao","family":"Pang","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, Columbia University, New York, NY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6499-7933","authenticated-orcid":false,"given":"Patrick","family":"Rockenschaub","sequence":"additional","affiliation":[{"name":"Institute of Clinical Epidemiology, Public Health, Health Economics, Medical Statistics and Informatics, Medical University of Innsbruck, Innsbruck, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0487-7454","authenticated-orcid":false,"given":"Pawel","family":"Renc","sequence":"additional","affiliation":[{"name":"AGH University of Krak\u00f3w, Krak\u00f3w, Poland"},{"name":"Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4804-6150","authenticated-orcid":false,"given":"Jungwoo","family":"Oh","sequence":"additional","affiliation":[{"name":"Kim Jaechul Graduate School of AI, Korea Advanced Institute of Science and Technology, Seoul"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2489-9615","authenticated-orcid":false,"given":"Kamil\u0117","family":"Stankevi\u010di\u016bt\u0117","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, University of Cambridge, Cambridge, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4700-6277","authenticated-orcid":false,"given":"Justin","family":"Xu","sequence":"additional","affiliation":[{"name":"Big Data Institute, University of Oxford, Oxford, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5676-7898","authenticated-orcid":false,"given":"Tom J.","family":"Pollard","sequence":"additional","affiliation":[{"name":"Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-9559-6216","authenticated-orcid":false,"given":"Nassim","family":"Oufattole","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2215-6527","authenticated-orcid":false,"given":"Michael","family":"Wornow","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Stanford University, Stanford, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1988-3478","authenticated-orcid":false,"given":"Teya S.","family":"Bergamaschi","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6552-5276","authenticated-orcid":false,"given":"Hyewon","family":"Jeong","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3324-9772","authenticated-orcid":false,"given":"Simon A.","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Computational Medicine, University of California, Los Angeles, Los Angeles, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6204-666X","authenticated-orcid":false,"given":"Vincent","family":"Jeanselme","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, Columbia University, New York, NY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8779-0209","authenticated-orcid":false,"given":"Kiril V.","family":"Klein","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Copenhagen, Copenhagen, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9072-0874","authenticated-orcid":false,"given":"Mikkel","family":"Odgaard","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Copenhagen, Copenhagen, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-3809-0513","authenticated-orcid":false,"given":"Maria E.","family":"Montgomery","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Copenhagen, Copenhagen, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0677-4002","authenticated-orcid":false,"given":"Arkadiusz","family":"Sitek","sequence":"additional","affiliation":[{"name":"Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1535-068X","authenticated-orcid":false,"given":"Mads","family":"Nielsen","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Copenhagen, Copenhagen, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6843-1355","authenticated-orcid":false,"given":"Jeffrey N.","family":"Chiang","sequence":"additional","affiliation":[{"name":"Department of Computational Medicine, University of California, Los Angeles, Los Angeles, USA"},{"name":"Department of Neurosurgery, University of California, Los Angeles, Los Angeles, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8811-7825","authenticated-orcid":false,"given":"Noa","family":"Dagan","sequence":"additional","affiliation":[{"name":"Clalit Research Institute, Clalit Health Services, Faculty of Computer and Information Science, Ramat-Gan, Israel"},{"name":"Ben-Gurion University of the Negev, Beersheba, Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2192-5160","authenticated-orcid":false,"given":"Isaac","family":"Kohane","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-0397-1346","authenticated-orcid":false,"given":"Shalmali","family":"Joshi","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, Columbia University, New York, NY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5958-3509","authenticated-orcid":false,"given":"Edward","family":"Choi","sequence":"additional","affiliation":[{"name":"Kim Jaechul Graduate School of AI, Korea Advanced Institute of Science and Technology, Seoul"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9385-7158","authenticated-orcid":false,"given":"Nigam H.","family":"Shah","sequence":"additional","affiliation":[{"name":"Center for Biomedical Informatics Research, Stanford University, Stanford, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"150","reference":[{"key":"e_1_3_4_2_2","unstructured":"McDermott M. The (lack of?) science of health AI. In: Proceedings of the 4th Machine Learning for Health Symposium. Vol 259. Vancouver BC: PMLR 2025:19-29 (https:\/\/proceedings.mlr.press\/v259\/mcdermott25a.html)."},{"key":"e_1_3_4_3_2","doi-asserted-by":"publisher","DOI":"10.1162\/99608f92.b91339ef"},{"key":"e_1_3_4_4_2","volume-title":"Tools and technologies for registry interoperability, registries for evaluating patient outcomes: a user\u2019s guide, 3rd edition, addendum 2","unstructured":"Gliklich RE, Leavy MB. Data standards. In: Gliklich RE, Leavy MB, Dreyer NA, eds. Tools and technologies for registry interoperability, registries for evaluating patient outcomes: a user\u2019s guide, 3rd edition, addendum 2. Rockville, MD: Agency for Healthcare Research and Quality, 2019 (https:\/\/www.ncbi.nlm.nih.gov\/books\/NBK551886\/)."},{"key":"e_1_3_4_5_2","volume-title":"Patient safety: achieving a New standard for care","author":"Institute of Medicine (U.S.) Committee on Data Standards for Patient Safety","unstructured":"Institute of Medicine (U.S.) Committee on Data Standards for Patient Safety. Health care data standards. In: Aspden P, Corrigan JM, Wolcott J, Erickson SM, eds. Patient safety: achieving a New standard for care. Washington, DC: National Academies Press, 2004 (https:\/\/www.ncbi.nlm.nih.gov\/books\/NBK216088\/)."},{"key":"e_1_3_4_6_2","doi-asserted-by":"publisher","DOI":"10.3233\/978-1-61499-564-7-574"},{"key":"e_1_3_4_7_2","doi-asserted-by":"publisher","DOI":"10.1136\/amiajnl-2011-000376"},{"key":"e_1_3_4_8_2","doi-asserted-by":"publisher","DOI":"10.1136\/amiajnl-2014-002747"},{"key":"e_1_3_4_9_2","unstructured":"Partners Healthcare System. Informatics for integrating biology to the bedside. i2b2 2026 (https:\/\/www.i2b2.org\/index.html)."},{"key":"e_1_3_4_10_2","doi-asserted-by":"publisher","DOI":"10.1056\/cat.21.0224"},{"key":"e_1_3_4_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(19)32317-7"},{"key":"e_1_3_4_12_2","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2018.1755"},{"key":"e_1_3_4_13_2","doi-asserted-by":"publisher","DOI":"10.1136\/bmjmed-2023-000651"},{"key":"e_1_3_4_14_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-024-01235-0"},{"key":"e_1_3_4_15_2","doi-asserted-by":"publisher","DOI":"10.1093\/gigascience\/giaf107"},{"key":"e_1_3_4_16_2","unstructured":"Waxler S Blazek P White D et al. Generative medical event models improve with scale. November 7 2025 (http:\/\/arxiv.org\/abs\/2508.12104). Preprint."},{"key":"e_1_3_4_17_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-025-09529-3"},{"key":"e_1_3_4_18_2","unstructured":"Pang C Jiang X Pavinkurve NP et al. CEHR-GPT: generating electronic health records with chronological patient timelines. May 6 2024 (http:\/\/arxiv.org\/abs\/2402.04400). Preprint."},{"key":"e_1_3_4_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2020.103637"},{"key":"e_1_3_4_20_2","unstructured":"Steinberg E Fries JA Xu Y Shah N. MOTOR: a time-to-event foundation model for structured medical records. In: The Twelfth International Conference on Learning Representations. Vienna Austria: ICLR 2024 (https:\/\/openreview.net\/forum?id=NialiwI2V6)."},{"key":"e_1_3_4_21_2","doi-asserted-by":"publisher","DOI":"10.1016\/S2589-7500(20)30186-2"},{"key":"e_1_3_4_22_2","doi-asserted-by":"crossref","unstructured":"McDermott MBA Xu J Bergamaschi TS et al. MEDS: building models and tools in a reproducible health AI ecosystem. In: Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V2. New York NY: ACM 2025:6243-6244 (https:\/\/dl.acm.org\/doi\/10.1145\/3711896.3737608).","DOI":"10.1145\/3711896.3737608"},{"key":"e_1_3_4_23_2","unstructured":"MEDS Working Group Arnrich B Choi E et al. Medical Event Data Standard (MEDS): facilitating machine learning for health. ICLR 2024 Workshop TS4H. Vienna Austria: ICLR 2024 (https:\/\/openreview.net\/forum?id=IsHy2ebjIG)."},{"key":"e_1_3_4_24_2","unstructured":"Wolf T Debut L Sanh V et al. HuggingFace\u2019s transformers: state-of-the-art natural language processing. July 14 2020 (http:\/\/arxiv.org\/abs\/1910.03771). Preprint."},{"key":"e_1_3_4_25_2","doi-asserted-by":"crossref","unstructured":"Marcel S Rodriguez Y. Torchvision the machine-vision package of torch. In: Proceedings of the International Conference on Multimedia \u2014 MM \u201910. New York NY: ACM Press 2010:1485-1488. DOI: 10.1145\/1873951.1874254.","DOI":"10.1145\/1873951.1874254"},{"key":"e_1_3_4_26_2","unstructured":"Bommasani R Hudson DA Adeli E et al. On the opportunities and risks of foundation models. July 12 2022 (http:\/\/arxiv.org\/abs\/2108.07258). Preprint."},{"key":"e_1_3_4_27_2","unstructured":"HL7 International. Fast healthcare interoperability resources (FHIR). Release 5. March 26 2023 (http:\/\/hl7.org\/fhir\/)."},{"key":"e_1_3_4_28_2","unstructured":"van de Water R. OMOP_MEDS: an ETL pipeline for transforming OMOP datasets into the MEDS format using the MEDS-Transforms library. Github April 3 2026 (https:\/\/github.com\/rvandewater\/OMOP_MEDS)."},{"key":"e_1_3_4_29_2","unstructured":"Github. meds_etl: a collection of ETLs from common data formats to Medical Event Data Standard. August 4 2025 (https:\/\/github.com\/Medical-Event-Data-Standard\/meds_etl)."},{"key":"e_1_3_4_30_2","unstructured":"van de Water R. FHIR2MEDS ETL (MEDS on FHIR). Github. July 21 2025. DOI: 10.5281\/zenodo.16269018."},{"key":"e_1_3_4_31_2","doi-asserted-by":"publisher","DOI":"10.17226\/27111"},{"key":"e_1_3_4_32_2","doi-asserted-by":"publisher","DOI":"10.1145\/3729215"},{"key":"e_1_3_4_33_2","doi-asserted-by":"publisher","DOI":"10.1145\/1327452.1327492"},{"key":"e_1_3_4_34_2","unstructured":"Xu J Gallifant J Johnson AEW McDermott MBA. ACES: automatic cohort extraction system for event-stream datasets. In: Proceedings of the International Conference on Learning Representations. Singapore: ICLR 2025 (https:\/\/openreview.net\/forum?id=P4XmKjXTrM)."},{"key":"e_1_3_4_35_2","unstructured":"Kolo A Pang C Choi E et al. MEDS decentralized extensible validation (MEDS-DEV) benchmark: establishing reproducibility and comparability in ML for health. ML4H 2024 Demo Track 2024 (https:\/\/openreview.net\/pdf?id=DExp3tRRel)."},{"key":"e_1_3_4_36_2","unstructured":"Oufattole N Bergamaschi T Kolo A et al. MEDS-Tab: automated tabularization and baseline methods for MEDS datasets. October 31 2024 (http:\/\/arxiv.org\/abs\/2411.00200). Preprint."},{"key":"e_1_3_4_37_2","unstructured":"Oufattole N Bergamaschi T Renc P Kolo A McDermott MBA Stultz C. MEDS-torch: an ML pipeline for inductive experiments for EHR medical foundation models. In: NeurIPS Workshop on Time Series in the Age of Large Models. Vancouver BC: NeurIPS 2024 (https:\/\/openreview.net\/forum?id=uwnK2LZtjX)."},{"key":"e_1_3_4_38_2","doi-asserted-by":"crossref","unstructured":"McDermott MBA Xu J Bergamaschi TS Jeong H Lee SA Oufattole N et al. MEDS: Building models and tools in a reproducible health AI ecosystem. In: Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V2. New York NY USA: ACM; 2025. pp. 6243-6244. DOI: 10.1145\/3711896.3737608.","DOI":"10.1145\/3711896.3737608"},{"key":"e_1_3_4_39_2","unstructured":"AlDanial Snel S Jolkdarr et al. AlDanial\/cloc: 1.92. Zenodo. December 6 2021. DOI: 10.5281\/ZENODO.5760077"},{"key":"e_1_3_4_40_2","unstructured":"Pang C Park J Jiang X et al. CEHR-XGPT: a scalable multi-task foundation model for electronic health records. September 5 2025 (http:\/\/arxiv.org\/abs\/2509.03643). Preprint."},{"key":"e_1_3_4_41_2","unstructured":"Fallahpour A Alinoori M Ye W Cao X Afkanpour A Krishnan A. EHRMamba: towards generalizable and scalable foundation models for electronic health records. In: Hegselmann S Zhou H Healey E Chang T Ellington C Mhasawade V Tonekaboni S Argaw P Zhang H eds. Proceedings of the 4th Machine Learning for Health Symposium. Vol 259. Singapore: PMLR 2025:291-307 (https:\/\/proceedings.mlr.press\/v259\/fallahpour25a.html)."},{"key":"e_1_3_4_42_2","unstructured":"Pang C Jeanselme V Choi YS et al. FoMoH: a clinically meaningful foundation model evaluation for structured electronic health records. June 16 2025 (http:\/\/arxiv.org\/abs\/2505.16941). Preprint."},{"key":"e_1_3_4_43_2","unstructured":"Steinberg E Wornow M Bedi S Fries J McDermott MBA Shah NH. Meds_reader: a fast and efficient EHR processing library. In: Machine Learning for Health Symposium (Findings Track). Vancouver BC: ML4H 2024 (https:\/\/arxiv.org\/abs\/2409.09095)."},{"key":"e_1_3_4_44_2","unstructured":"Hegselmann S von Arnim G Rheude T et al. Large language models are powerful electronic health record encoders. April 13 2025 (http:\/\/arxiv.org\/abs\/2502.17403). Preprint."},{"key":"e_1_3_4_45_2","unstructured":"Zhang Y Li S. ChronoFormer: time-aware transformer architectures for structured clinical event modeling. April 10 2025 (http:\/\/arxiv.org\/abs\/2504.07373). Preprint."},{"key":"e_1_3_4_46_2","doi-asserted-by":"crossref","unstructured":"Pham KM Mai TT Dao TTP Tran M-T Crane M Bezbradica M. Zero-shot detection of nosocomial infections using clinical foundation models on structured electronic patient records. June 18 2025 (https:\/\/doi.org\/10.36227\/techrxiv.175021755.55836808\/v1). Preprint.","DOI":"10.36227\/techrxiv.175021755.55836808\/v1"},{"key":"e_1_3_4_47_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-025-01777-x"},{"key":"e_1_3_4_48_2","doi-asserted-by":"publisher","DOI":"10.21105\/joss.08869"},{"key":"e_1_3_4_49_2","unstructured":"Zhu M Luo Z Liu Y Zhu T. MedTPE: compressing long EHR sequence for LLM-based clinical prediction with token-pair encoding. Presented at The 11th Mining and Learning from Time Series Workshop. Toronto ON: MILETS August 3-7 2025 (https:\/\/kdd-milets.github.io\/milets2025\/papers\/MILETS_2025_paper_16.pdf)."},{"key":"e_1_3_4_50_2","unstructured":"Fani R Attrach RA Restrepo D Jia Y Celi LA Sch\u00fcffler P. Coefficient of variation masking: a volatility-aware strategy for EHR foundation models. In: Proceedings of the 5th Machine Learning for Health Symposium. Singapore: PMLR December 4 2025 (https:\/\/arxiv.org\/abs\/2512.05216)."},{"key":"e_1_3_4_51_2","unstructured":"Marfoglia A Jhee JH Coulet A. Clinical data goes MEDS? Let\u2019s OWL make sense of it. February 2 2026 (https:\/\/doi.org\/10.48550\/arXiv.2601.04164). Preprint."},{"key":"e_1_3_4_52_2","doi-asserted-by":"crossref","unstructured":"Zhu J Feng X Shen Y Ning G Fang L Pan Q. PMTG: personalized multivariate temporal graph learning with multi-granular encoding for clinical prediction on EHRs. In: 2025 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). Wuhan China: IEEE 2025:5589-5596 (https:\/\/www.semanticscholar.org\/paper\/PMTG%3A-Personalized-Multivariate-Temporal-Graph-with-Zhu-Feng\/f05fb2ecc5650326da63a3a41f0b82865ba793dd).","DOI":"10.1109\/BIBM66473.2025.11356691"},{"key":"e_1_3_4_53_2","doi-asserted-by":"crossref","unstructured":"Zhang Y Zhou H Feng L Hong Y Li S. Learning longitudinal health representations from EHR and wearable data. January 18 2026 (https:\/\/doi.org\/10.48550\/arXiv.2601.12227). Preprint.","DOI":"10.36227\/techrxiv.176948890.08042597\/v1"},{"key":"e_1_3_4_54_2","unstructured":"Cho E Kim J Lee M Park S Choi E. Generating multi-table time series EHR from latent space with minimal preprocessing. The Thirty-Ninth Annual Conference on Neural Information Processing Systems. San Diego CA: NeurIPS April 21 2026 (https:\/\/openreview.net\/forum?id=k5TbfYPYuc)."},{"key":"e_1_3_4_55_2","doi-asserted-by":"crossref","unstructured":"Hu Y Chuan C Zhang W et al. Icom: an intervention based foundation model for healthcare. January 9 2026 (https:\/\/doi.org\/10.36227\/techrxiv.176800116.62500980\/v1). Preprint.","DOI":"10.36227\/techrxiv.176800116.62500980\/v1"},{"key":"e_1_3_4_56_2","doi-asserted-by":"crossref","unstructured":"Alonso E Mendez N Garcia-Navarro T et al. EHRs enable robust lung cancer risk stratification with transformer-based models: a retrospective multi-center validation study. December 2 2025 (https:\/\/doi.org\/10.21203\/rs.3.rs-8200847\/v1).","DOI":"10.21203\/rs.3.rs-8200847\/v1"},{"key":"e_1_3_4_57_2","unstructured":"Lowe S Park G Lee L Smith P. Latent physiology as language: a state-space foundation model for multimodal ICU and EHR representation learning. 2026. DOI: 10.13140\/RG.2.2.21597.27364."},{"key":"e_1_3_4_58_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-026-02337-7"},{"key":"e_1_3_4_59_2","unstructured":"Qian L. On AI frameworks for healthcare time-series analysis: missingness dynamics and representation. Kings College London August 2025 (https:\/\/kclpure.kcl.ac.uk\/ws\/portalfiles\/portal\/351130102\/2025_Qian_Linglong_1920412_ethesis.pdf)."},{"key":"e_1_3_4_60_2","doi-asserted-by":"crossref","unstructured":"Lawrence S Zhang Y Wang M. Markovian representation learning for longitudinal electronic health records. Research Square February 24 2026 (https:\/\/doi.org\/10.21203\/rs.3.rs-8935515\/v1).","DOI":"10.21203\/rs.3.rs-8935515\/v1"},{"key":"e_1_3_4_61_2","doi-asserted-by":"crossref","unstructured":"Ferrel W Chang T Lawrence S. Sparse foundation models for continuous-time EHRs. February 26 2026 (https:\/\/doi.org\/10.21203\/rs.3.rs-8819405\/v1).","DOI":"10.21203\/rs.3.rs-8819405\/v1"},{"key":"e_1_3_4_62_2","unstructured":"McDermott M Xu J. MIMIC-IV MEDS: an ETL pipeline to extract MIMIC-IV data into the MEDS format. Github. November 5 2025. DOI: 10.5281\/zenodo.17535580."},{"key":"e_1_3_4_63_2","unstructured":"Rockenschaub P. AUMCdb_MEDS: the AUMCdb MEDS ETL. Github. November 5 2025 (https:\/\/github.com\/prockenschaub\/AUMCdb_MEDS)."},{"key":"e_1_3_4_64_2","unstructured":"van de Water RP. NWICU_MEDS ETL. Github. March 14 2025. DOI: 10.5281\/zenodo.14892134."},{"key":"e_1_3_4_65_2","unstructured":"van de Water RP. SICdb_MEDS ETL. Github. February 19 2025. DOI: 10.5281\/zenodo.14893939."},{"key":"e_1_3_4_66_2","unstructured":"van de Water RP. INSPIRE_MEDS ETL. Github. February 19 2025. DOI: 10.5281\/zenodo.14891940."},{"key":"e_1_3_4_67_2","unstructured":"McDermott M Zhu M van de Water RP. eICU_MEDS: eICU MEDS Extraction ETL. Github. November 5 2025. DOI: 10.5281\/zenodo.17535692."},{"key":"e_1_3_4_68_2","unstructured":"Github. van de Water RP. HIRID MEDS ETL. October 16 2025. DOI: 10.5281\/zenodo.17370179."},{"key":"e_1_3_4_69_2","unstructured":"Github. femr: FEMR (Framework for Electronic Medical Records) provides tooling for large-scale self-supervised learning using electronic health records. October 16 2025 (https:\/\/github.com\/som-shahlab\/femr)."},{"key":"e_1_3_4_70_2","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2023.3327951"},{"key":"e_1_3_4_71_2","unstructured":"Pang C Jiang X Kalluri KS et al. CEHR-BERT: incorporating temporal information from structured EHR data to improve prediction tasks. In: Roy S Pfohl S Rocheteau E Tadesse GA Oala L Falck F et al. eds. Proceedings of Machine Learning for Health. Vol 158. PMLR 2021:239-260 (https:\/\/proceedings.mlr.press\/v158\/pang21a.html)."},{"key":"e_1_3_4_72_2","unstructured":"Odgaard M Klein K Thysen SM Jimenez-Solem E Sillesen M Nielsen M. CORE-BEHRT: a carefully optimized and rigorously evaluated BEHRT. In: Deshpande K Fiterau M Joshi S Lipton Z Ranganath R Urteaga I eds. Machine Learning for Healthcare Conference. Vienna Austria: PMLR October 11 2024. DOI: 10.48550\/arXiv.2404.15201."},{"key":"e_1_3_4_73_2","unstructured":"McDermott M. MEDS \u201ceverything-is-code\u201d autoregressive model. Github November 5 2025. DOI: 10.5281\/zenodo.17535559."},{"key":"e_1_3_4_74_2","unstructured":"Fries J. meds-mcp: MEDS Model Context Protocol (MCP) server and client. Github April 29 2026 (https:\/\/github.com\/VISTA-Stanford\/meds-mcp)."},{"key":"e_1_3_4_75_2","unstructured":"van de Water R. MEDS-Inspect. Github. October 27 2025. DOI: 10.5281\/zenodo.14892253."},{"key":"e_1_3_4_76_2","unstructured":"McDermott M Oufattole N Bergamaschi T Chandak P King R Kolo A. MEDS TorchData. Github. April 21 2026. DOI: 10.5281\/zenodo.17535772."},{"key":"e_1_3_4_77_2","unstructured":"Wornow M Bedi S Hernandez MAF et al. Context clues: evaluating long context models for clinical prediction tasks on EHR data. In: The Thirteenth International Conference on Learning Representations. Singapore: ICLR February 13 2025 (https:\/\/openreview.net\/forum?id=zg3ec1TdAP)."},{"key":"e_1_3_4_78_2","doi-asserted-by":"crossref","unstructured":"Yang C Wu Z Jiang P et al. Pyhealth: a deep learning toolkit for healthcare applications. In: Proceedings of the 29th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) 2023. New York NY: Association of Computing Machinery 2023:5788-5789 (https:\/\/dl.acm.org\/doi\/10.1145\/3580305.3599178).","DOI":"10.1145\/3580305.3599178"},{"key":"e_1_3_4_79_2","unstructured":"McDermott MBA Nestor B Argaw P Kohane I. Event stream GPT: a data pre-processing and modeling library for generative pre-trained transformers over continuous-time sequences of complex events. In: Proceedings of the Thirty-Seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track. New Orleans LA: NeurIPS 2023 (https:\/\/papers.neurips.cc\/paper_files\/paper\/2023\/file\/4c8f197b24e9b05d22028c2de16a45d2-Paper-Datasets_and_Benchmarks.pdf)."},{"key":"e_1_3_4_80_2","unstructured":"van de Water R Schmidt H Elbers P Thoral P Arnrich B Rockenschaub P. Yet another ICU benchmark: a flexible multi-center framework for clinical ML. In: The Twelfth International Conference on Learning Representations. Vienna Austria: ICLR March 15 2024 (https:\/\/openreview.net\/forum?id=ox2ATRM90I)."},{"key":"e_1_3_4_81_2","unstructured":"Jarrett D Yoon J Bica I Qian Z Ercole A van der Schaar M. Clairvoyance: a pipeline toolkit for medical time series. In: The Ninth International Conference on Learning Representations Editor. Austria: ICLR 2021 (https:\/\/openreview.net\/pdf?id=xnC8YwKUE3k)."},{"key":"e_1_3_4_82_2","unstructured":"Saveliev ES van der Schaar M. TemporAI: facilitating machine learning innovation in time domain tasks for medicine. January 28 2023 (http:\/\/arxiv.org\/abs\/2301.12260). Preprint."},{"key":"e_1_3_4_83_2","doi-asserted-by":"crossref","unstructured":"Wang S McDermott MBA Chauhan G Ghassemi M Hughes MC Naumann T. MIMIC-Extract: a data extraction preprocessing and representation pipeline for MIMIC-III. Proceedings of the ACM Conference on Health Inference and Learning. New York NY USA: Association for Computing Machinery 2020:222-235. DOI: 10.1145\/3368555.3384469.","DOI":"10.1145\/3368555.3384469"},{"key":"e_1_3_4_84_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.emnlp-main.1245"},{"key":"e_1_3_4_85_2","unstructured":"Attrach RA Moreira P Fani R Umeton R Celi LA. Conversational LLMs simplify secure clinical data access understanding and analysis. November 23 2025 (http:\/\/arxiv.org\/abs\/2507.01053). Preprint."},{"key":"e_1_3_4_86_2","unstructured":"Fries J. meds2text: transforms MEDS-formatted data to textual representations. Github. December 22 2025 (https:\/\/github.com\/VISTA-Stanford\/meds2text)."}],"container-title":["NEJM AI"],"original-title":[],"language":"en","deposited":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T12:53:34Z","timestamp":1781182414000},"score":1,"resource":{"primary":{"URL":"https:\/\/ai.nejm.org\/doi\/10.1056\/AIra2501253"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,28]]},"references-count":85,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2026,5,28]]}},"alternative-id":["10.1056\/AIra2501253"],"URL":"https:\/\/doi.org\/10.1056\/aira2501253","relation":{},"ISSN":["2836-9386"],"issn-type":[{"value":"2836-9386","type":"print"}],"subject":[],"published":{"date-parts":[[2026,5,28]]}}}