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High-throughput multimodal automated phenotyping (MAP) with application to PheWAS., , , , , , , , , and 9 other author(s). J. Am. Medical Informatics Assoc., 26 (11): 1255-1262 (2019)Research and applications: Word sense disambiguation in the clinical domain: a comparison of knowledge-rich and knowledge-poor unsupervised methods., , , and . J. Am. Medical Informatics Assoc., 21 (5): 842-849 (2014)3D-MICE: integration of cross-sectional and longitudinal imputation for multi-analyte longitudinal clinical data., , , and . J. Am. Medical Informatics Assoc., 25 (6): 645-653 (2018)Unsupervised Multimodal Representation Learning across Medical Images and Reports., , , , and . CoRR, (2018)Is BERT Really Robust? Natural Language Attack on Text Classification and Entailment., , , and . CoRR, (2019)Unsupervised Clinical Language Translation., , and . KDD, page 3121-3131. ACM, (2019)Predicting Clinical Outcomes Across Changing Electronic Health Record Systems., , , and . KDD, page 1497-1505. ACM, (2017)Right, No Matter Why: AI Fact-checking and AI Authority in Health-related Inquiry Settings., , , , and . CoRR, (2023)A comprehensive EHR timeseries pre-training benchmark., , , , , , and . CHIL, page 257-278. ACM, (2021)Do We Still Need Clinical Language Models?, , , , , , , , , and . CHIL, volume 209 of Proceedings of Machine Learning Research, page 578-597. PMLR, (2023)