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Assessing clinical heterogeneity in sepsis through treatment patterns and machine learning., , , , , , and . J. Am. Medical Informatics Assoc., 26 (12): 1466-1477 (2019)Unsupervised probabilistic models for sequential Electronic Health Records., , , and . J. Biomed. Informatics, (2022)Nonstationary multivariate Gaussian processes for electronic health records., , , , , and . J. Biomed. Informatics, (2021)Leveraging EHR Audit Log Data to Unlock New Insights into Care Processes and Outcomes., , , , , , , , , and . AMIA, AMIA, (2022)Context is Key: Using the Audit Log to Capture Contextual Factors Affecting Stroke Care Processes., , , , , , , , and . AMIA, AMIA, (2020)A novel propensity modeling approach to estimate the causal impact of acute organ dysfunction on long-term survival in sepsis., , , , , , and . CRI, AMIA, (2017)The number needed to benefit: estimating the value of predictive analytics in healthcare., , , and . J. Am. Medical Informatics Assoc., 26 (12): 1655-1659 (2019)Developing a shared sepsis data infrastructure: a systematic review and concept map to FHIR., , , , , , , , , and 1 other author(s). npj Digit. Medicine, (2022)Flexible, cluster-based analysis of the electronic medical record of sepsis with composite mixture models., , , , , and . J. Biomed. Informatics, (2018)Automated identification of pneumonia in chest radiograph reports in critically ill patients., , , , , , and . BMC Medical Informatics Decis. Mak., (2013)