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Risk prediction for chronic kidney disease progression using heterogeneous electronic health record data and time series analysis.

, , , , and . J. Am. Medical Informatics Assoc., 22 (4): 872-880 (2015)

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The Survival Filter: Joint Survival Analysis with a Latent Time Series., , , and . UAI, page 742-751. AUAI Press, (2015)Temporal trends of hemoglobin A1c testing., , , , and . JAMIA, 21 (6): 1038-1044 (2014)Deep Survival Analysis., , , and . MLHC, volume 56 of JMLR Workshop and Conference Proceedings, page 101-114. JMLR.org, (2016)Machine Learning and Visualization in Clinical Decision Support: Current State and Future Directions., , and . CoRR, (2019)We Make Choices We Think are Going to Save Us: Debate and Stance Identification for Online Breast Cancer CAM Discussions., , , , , and . WWW (Companion Volume), page 1073-1081. ACM, (2017)Exploring Gender Disparities in Time to Diagnosis., , , , and . CoRR, (2020)A generative, predictive model for menstrual cycle lengths that accounts for potential self-tracking artifacts in mobile health data., , , , , and . CoRR, (2021)From Sparse to Dense: GPT-4 Summarization with Chain of Density Prompting., , , , and . CoRR, (2023)Detecting Social and Behavioral Determinants of Health with Structured and Free-Text Clinical Data., , , , , and . Appl. Clin. Inform., 11 (01): 172-181 (2020)Augmenting the Clinical Data Sources for Enigmatic Diseases: A Cross-Sectional Study of Self-Tracking Data and Clinical Documentation in Endometriosis., , , , and . Appl. Clin. Inform., 11 (05): 769-784 (2020)