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Clinical Concept Embeddings Learned from Massive Sources of Medical Data., , , , , , and . CoRR, (2018)sureLDA: A multidisease automated phenotyping method for the electronic health record., , , , , , , , and . J. Am. Medical Informatics Assoc., 27 (8): 1235-1243 (2020)Prior Adaptive Semi-supervised Learning with Application to EHR Phenotyping., , , and . J. Mach. Learn. Res., (2022)ATLAS: an automated association test using probabilistically linked health records with application to genetic studies., , , , , , , , , and . J. Am. Medical Informatics Assoc., 28 (12): 2582-2592 (2021)BELT: Blockwise Missing Embedding Learning Transfomer., , and . CoRR, (2021)Multimodal Learning on Graphs for Disease Relation Extraction., , , , and . CoRR, (2022)SurvMaximin: Robust federated approach to transporting survival risk prediction models., , , , , , , , , and 48 other author(s). J. Biomed. Informatics, (2022)International electronic health record-derived COVID-19 clinical course profiles: the 4CE consortium., , , , , , , , , and 77 other author(s). npj Digit. Medicine, (2020)Surrogate-assisted feature extraction for high-throughput phenotyping., , , , , , , , , and 1 other author(s). JAMIA, 24 (e1): e143-e149 (2017)Clinical Concept Embeddings Learned from Massive Sources of Multimodal Medical Data, , , , , , , , and . (2018)cite arxiv:1804.01486.