Bayesian Learning of Clinically Meaningful Sepsis Phenotypes in Northern Tanzania
arxiv(2024)
摘要
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. In this
article, 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 medically significant 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.
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