BAYESIAN LEARNING OF CLINICALLY MEANINGFUL SEPSIS PHENOTYPES IN NORTHERN TANZANIA.

IF 1.4 4区 数学 Q2 STATISTICS & PROBABILITY
Annals of Applied Statistics Pub Date : 2025-09-01 Epub Date: 2025-08-28 DOI:10.1214/25-aoas2045
Alexander Dombowsky, David B Dunson, Deng B Madut, Matthew P Rubach, Amy H Herring
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引用次数: 0

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. 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.

坦桑尼亚北部临床意义的败血症表型的贝叶斯学习。
败血症是一种危及生命的疾病,由宿主对感染的反应失调引起。最近,研究人员假设脓毒症由不同亚型的异质谱组成,这促使一些研究确定与亚型相对应的脓毒症患者群,并利用这些群设计亚型特异性治疗的长期目标。因此,临床医生依赖具有具体医学解释的聚类,通常对应于对从业者具有具体含义的样本空间中有临床意义的区域。在本文中,我们提出了围绕有意义区域的聚类(CLAMR),这是一种贝叶斯聚类方法,它明确地模拟了每个聚类中心的医学解释。CLAMR倾向于可以通过有意义的特征值进行总结的聚类,从而导致具有医学意义的脓毒症患者聚类。我们还提供了使用贝叶斯假设检验测量每个特征对聚类的影响的详细信息,因此可以评估哪些特征与聚类解释相关。我们的重点是来自坦桑尼亚Moshi的聚类脓毒症患者,那里的患者更年轻,HIV感染的流行率高于以前的脓毒症亚型队列。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Annals of Applied Statistics
Annals of Applied Statistics 社会科学-统计学与概率论
CiteScore
3.10
自引率
5.60%
发文量
131
审稿时长
6-12 weeks
期刊介绍: Statistical research spans an enormous range from direct subject-matter collaborations to pure mathematical theory. The Annals of Applied Statistics, the newest journal from the IMS, is aimed at papers in the applied half of this range. Published quarterly in both print and electronic form, our goal is to provide a timely and unified forum for all areas of applied statistics.
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