Predicting sepsis onset using a machine learned causal probabilistic network algorithm based on electronic health records data.

ABSTRACT: Sepsis is a leading cause of mortality and early identification improves survival. With increasing digitalization of health care data automated sepsis prediction models hold promise to aid in prompt recognition. Most previous studies have focused on the intensive care unit (ICU) setting. Yet only a small proportion of sepsis develops in the ICU and there is an apparent clinical benefit to identify patients earlier in the disease trajectory. In this cohort of 82,852 hospital admissions and 8038 sepsis episodes classified according to the Sepsis-3 criteria, we demonstrate that a machine learned score can predict sepsis onset within 48 h using sparse routine electronic health record data outside the ICU. Our score was based on a causal probabilistic network model-SepsisFinder-which has simila

SUBMITTER: Valik JK 

PROVIDER: S-EPMC10359402 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature