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Approaches for using temporal and other filters for next generation phenotype discovery.

, , and . AMIA, AMIA, (2016)

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Multi-Task Gaussian Processes and Dilated Convolutional Networks for Reconstruction of Reproductive Hormonal Dynamics., , , , and . MLHC, volume 106 of Proceedings of Machine Learning Research, page 66-90. PMLR, (2019)Data assimilation of glucose dynamics for use in the intensive care unit., , and . EMBC, page 5437-5440. IEEE, (2012)Personal Health Oracle: Explorations of Personalized Predictions in Diabetes Self-Management., , , , , and . CHI, page 370. ACM, (2019)A methodology of phenotyping ICU patients from EHR data: High-fidelity, personalized, and interpretable phenotypes estimation., , , , , , , , , and . J. Biomed. Informatics, (December 2023)Lessons learned from assimilating knowledge into machine learning to forecast and control glucose in a critical care setting., , , , , , , and . AMIA, AMIA, (2020)Using data assimilation to forecast post-meal glucose for patients with type 2 diabetes., , , , and . AMIA, AMIA, (2016)Comparing Lagged Linear Correlation, Lagged Regression, Granger Causality, and Vector Autoregression for Uncovering Associations in EHR Data., , and . AMIA, AMIA, (2016)Approaches for using temporal and other filters for next generation phenotype discovery., , and . AMIA, AMIA, (2016)Model Selection For EHR Laboratory Tests Preserving Healthcare Context and Underlying Physiology., , , , and . AMIA, AMIA, (2015)Exploiting time in electronic health record correlations., , and . JAMIA, 18 (Supplement): 109-115 (2011)