Colloquium: Bayesian Learning of Clinically Meaningful Sepsis Phenotypes

Dr. Herring’s talk is entitled “Bayesian Learning of Clinically Meaningful Sepsis Phenotypes” presented next Tuesday, April 1 at 11 AM in Divinity 202

Abstract: Sepsis is a life-threatening condition caused by a dysregulated host response to infection. Recently, researchers have hypothesized that sepsis consists of a heterogeneous spectrum of distinct subtypes, motivating several studies to identify clusters of sepsis patients that correspond to subtypes, with the long-term goal of using these clusters to design subtype-specific treatments. Therefore, clinicians rely on clusters having a concrete medical interpretation, usually corresponding to clinically meaningful regions of the sample space that have a concrete implication to practitioners. We propose Clustering Around Meaningful Regions (CLAMR), a Bayesian clustering approach that explicitly models the medical interpretation of each cluster center. CLAMR favors clusterings that can be summarized via meaningful feature values, leading to (more) medically interpretable sepsis patient clusters. We also provide details on measuring the effect of each feature on the clustering using Bayesian hypothesis tests, so one can assess what features are relevant for cluster interpretation. Our focus is on clustering sepsis patients from Moshi, Tanzania, where patients are younger and the prevalence of HIV infection is higher than in previous sepsis subtyping cohorts.

Amy H. Herring is Sara & Charles Ayres Distinguished Professor of Statistical Science, Global Health, and Biostatistics and Bioinformatics at Duke University. Dr. Herring received her doctorate in biostatistics at Harvard University and came to Duke from UNC-Chapel Hill, where she was distinguished professor of biostatistics. Her research interests include development of statistical methodology for longitudinal or clustered data, Bayesian methods, latent class and latent variable models, missing data, complex environmental mixtures, and applications of statistics in population health and medicine. She has received numerous awards for her work, including the Mortimer Spiegelman Award from the American Public Health Association as the best applied public health statistician under age 40. Her research program is funded by NIH, and she holds leadership positions at the national and international level, including as Chair of the American Statistical Association’s Section on Bayesian Statistical Science, as President of the International Society for Bayesian Analysis, and as a member of the Board of the International Biometric Society.