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Lessons learned from assimilating knowledge into machine learning to forecast and control glucose in a critical care setting., , , , , , , и . AMIA, AMIA, (2020)A methodology of phenotyping ICU patients from EHR data: High-fidelity, personalized, and interpretable phenotypes estimation., , , , , , , , , и . J. Biomed. Informatics, (декабря 2023)Estimating summary statistics for electronic health record laboratory data for use in high-throughput phenotyping algorithms., , , , , и . J. Biomed. Informatics, (2018)Automated Identification of Causal Moderators in Time-Series Data., , и . CD@KDD, том 92 из Proceedings of Machine Learning Research, стр. 4-22. PMLR, (2018)Imputation of Missing Values in Time Series with Lagged Correlations., , , и . ICDM Workshops, стр. 753-762. IEEE Computer Society, (2014)A generalizable physiological model for detection of Delayed Cerebral Ischemia using Federated Learning., , , , , , , , , и 5 other автор(ы). BIBM, стр. 1886-1889. IEEE, (2023)Lagged Correlations among Physiological Variables as Indicators of Consciousness in Stroke Patients., , и . AMIA, AMIA, (2019)Using mechanistic machine learning to forecast glucose and infer physiologic phenotypes in the ICU: what is possible and what are the challenges., , , , , и . AMIA, AMIA, (2018)Using Empirical orthogonal functions to identify temporally important variables to understand time-dependent pathophysiologic and phenotypic differences in patients., , и . AMIA, AMIA, (2012)