Title: Modeling sepsis progression using hidden Markov models,
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.