English

Modeling sepsis progression using hidden Markov models

Machine Learning 2018-01-10 v1 Quantitative Methods

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.

Keywords

Cite

@article{arxiv.1801.02736,
  title  = {Modeling sepsis progression using hidden Markov models},
  author = {Brenden K. Petersen and Michael B. Mayhew and Kalvin O. E. Ogbuefi and John D. Greene and Vincent X. Liu and Priyadip Ray},
  journal= {arXiv preprint arXiv:1801.02736},
  year   = {2018}
}

Comments

Accepted to NIPS ML4H 2017

R2 v1 2026-06-22T23:39:56.611Z