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Assessing the effects of data drift on the performance of machine learning models used in clinical sepsis prediction., , , , , , and . Int. J. Medical Informatics, (May 2023)Workload-Aware Approximate Computing Configuration., , , , and . DATE, page 920-925. IEEE, (2021)DEPLOYR: A technical framework for deploying custom real-time machine learning models into the electronic medical record., , , , , , , , and . CoRR, (2023)The Shaky Foundations of Clinical Foundation Models: A Survey of Large Language Models and Foundation Models for EMRs., , , , , , , , and . CoRR, (2023)The shaky foundations of large language models and foundation models for electronic health records., , , , , , , , and . npj Digit. Medicine, (2023)Evaluation of GPT-3.5 and GPT-4 for supporting real-world information needs in healthcare delivery., , , , , , , , , and 8 other author(s). CoRR, (2023)SpamHD: Memory-Efficient Text Spam Detection using Brain-Inspired Hyperdimensional Computing., , , and . ISVLSI, page 84-89. IEEE, (2021)MedAlign: A Clinician-Generated Dataset for Instruction Following with Electronic Medical Records., , , , , , , , , and 20 other author(s). CoRR, (2023)Standing on FURM ground - A framework for evaluating Fair, Useful, and Reliable AI Models in healthcare systems., , , , , , , , , and 14 other author(s). CoRR, (2024)OpenMedLM: Prompt engineering can out-perform fine-tuning in medical question-answering with open-source large language models., , , , , , , , , and . CoRR, (2024)