Adler Perotte

BioOHDSI Publications

Adler Perotte, MD, MA
Assistant Professor, Department of Biomedical Informatics
Columbia University

Dr. Perotte’s primary research areas are Bayesian inference and prediction related to electronic health record data and metabolomics. His research interests are both in the development of novel statistical machine learning methods with broad applicability across domains and the use of such methods towards solving biomedical problems. Specific areas of interest include probabilistic phenotyping, statistical natural language processing, time series modeling, cohort selection, causal inference and analysis of high frequency monitoring data.

As a collaborator in the Observation Health Data Sciences and Informatics (OHDSI) collaborative Dr. Perotte has been very involved in the activities of the coordinating center at Columbia University.

Hripcsak G, Ryan P, Duke J, Shah NH, Park RW, Huser V, Suchard MA, Schuemie M, DeFalco F, Perotte A, Banda J, Reich C, Schilling L, Matheny M, Meeker D, Pratt N, Madigan D. Addressing Clinical Questions at Scale: OHDSI Assessment of Treatment Pathways. PNAS, submitted, 2015.

Pivovarov R, Perotte A, Grave E, Angiolillo J, Wiggins C, Elhadad N. Learning Probabilistic Phenotypes from Heterogeneous EHR Data. J Biomed Inform., in press, 2015.

Hripcsak G, Albers DJ, Perotte A. Parameterizing time in electronic health record studies. J Am Med Inform Assoc. 2015 Feb 26. pii: ocu051. doi: 10.1093/jamia/ocu051.

Perotte A, Ranganath R, Hirsch JS, Blei D, Elhadad N. Risk prediction for chronic kidney disease progression using heterogeneous electronic health record data and time series analysis. J Am Med Inform Assoc. 2015 Apr 20. pii: ocv024. doi: 10.1093/jamia/ocv024.

Perotte A, Pivovarov R, Natarajan K, Weiskopf N, Wood F, Elhadad N. Diagnosis code assignment: models and evaluation metrics. J Am Med Inform Assoc., Dec 2013. doi: 10.1136/amiajnl-2013-002159

Ranganath R, Perotte A, Elhadad N, Blei D. The Survival Filter: Joint Survival Analysis with a Latent Time Series. Uncertainty in Artificial Intelligence, Amsterdam, Netherlands, 2015.