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Predictive Modeling of Hospital Readmission Rates Using Electronic Medical Record-Wide Machine Learning: A Case-Study Using Mount Sinai Heart Failure Cohort.

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Machine Learning for Health (ML4H) Workshop at NeurIPS 2018., , , , , , , , , and 8 other author(s). CoRR, (2018)Using model classifications as quantitative traits to estimate stroke heritability., , , , , , , , , and . AMIA, AMIA, (2018)Improving user engagement and insight through Contextualized Quantified Self., , and . AMIA, AMIA, (2015)Predictive Modeling of Hospital Readmission Rates Using Electronic Medical Record-Wide Machine Learning: A Case-Study Using Mount Sinai Heart Failure Cohort., , , , , , , , , and 7 other author(s). PSB, page 276-287. (2017)Automated Identification of Families in Electronic Health Records to Support Clinical Research., , , , , , , , , and 3 other author(s). CRI, AMIA, (2017)Simulating drug responses in laboratory test time series with deep generative modeling.. Columbia University, USA, (2019)Generative Adversarial Networks for Electronic Health Records: A Framework for Exploring and Evaluating Methods for Predicting Drug-Induced Laboratory Test Trajectories., , , and . CoRR, (2017)Aurigo: an Interactive Tour Planner for Personalized Itineraries., , , , and . IUI, page 275-285. ACM, (2015)