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Self-supervised machine learning using adult inpatient data produces effective models for pediatric clinical prediction tasks.

, , , , , , , , , , and . J. Am. Medical Informatics Assoc., 30 (12): 2004-2011 (November 2023)

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PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts., , , , , , , , , and 16 other author(s). CoRR, (2022)Snorkel: Rapid Training Data Creation with Weak Supervision., , , , , and . Proc. VLDB Endow., 11 (3): 269-282 (2017)PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts., , , , , , , , , and 17 other author(s). ACL (demo), page 93-104. Association for Computational Linguistics, (2022)Characterizing the limitations of using diagnosis codes in the context of machine learning for healthcare., , , , , , , , , and 1 other author(s). BMC Medical Informatics Decis. Mak., 24 (1): 51 (December 2024)BigBIO: A Framework for Data-Centric Biomedical Natural Language Processing., , , , , , , , , and 33 other author(s). CoRR, (2022)The accuracy vs. coverage trade-off in patient-facing diagnosis models., , , , , and . CoRR, (2019)Systematic Review of Approaches to Preserve Machine Learning Performance in the Presence of Temporal Dataset Shift in Clinical Medicine., , , , , , , and . Appl. Clin. Inform., 12 (4): 808-815 (2021)Brundlefly at SemEval-2016 Task 12: Recurrent Neural Networks vs. Joint Inference for Clinical Temporal Information Extraction.. SemEval@NAACL-HLT, page 1274-1279. The Association for Computer Linguistics, (2016)Multi-Resolution Weak Supervision for Sequential Data., , , , , , , , , and 1 other author(s). NeurIPS, page 192-203. (2019)The shaky foundations of large language models and foundation models for electronic health records., , , , , , , , and . npj Digit. Medicine, (2023)