Assessing clinical heterogeneity in sepsis through treatment patterns and machine learning.

ABSTRACT:

Objective

To use unsupervised topic modeling to evaluate heterogeneity in sepsis treatment patterns contained within granular data of electronic health records.

Materials and methods

A multicenter, retrospective cohort study of 29 253 hospitalized adult sepsis patients between 2010 and 2013 in Northern California. We applied an unsupervised machine learning method, Latent Dirichlet Allocation, to the orders, medications, and procedures recorded in the electronic health record within the first 24 hours of each patient's hospitalization to uncover empiric treatment topics across the cohort and to develop computable clinical signatures for each patient based on proportions of these topics. We evaluated how these topics correlated with common sepsis treatment and outcome metrics

SUBMITTER: Fohner AE 

PROVIDER: S-EPMC7647146 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature