Modeling sepsis progression using hidden Markov models

TL;DR

This paper introduces a hidden Markov model to better understand and predict sepsis progression by capturing patient heterogeneity, aiding in risk stratification and personalized treatment decisions.

Contribution

The novel model explicitly accounts for patient heterogeneity in sepsis progression, improving upon existing diagnostic criteria.

Findings

Model effectively uncovers latent sepsis trajectories.

Outperforms traditional diagnostic criteria in identifying high-risk patients.

Provides a tool for personalized treatment planning.

Abstract

Characterizing a patient's progression through stages of sepsis is critical for enabling risk stratification and adaptive, personalized treatment. However, commonly used sepsis diagnostic criteria fail to account for significant underlying heterogeneity, both between patients as well as over time in a single patient. We introduce a hidden Markov model of sepsis progression that explicitly accounts for patient heterogeneity. Benchmarked against two sepsis diagnostic criteria, the model provides a useful tool to uncover a patient's latent sepsis trajectory and to identify high-risk patients in whom more aggressive therapy may be indicated.