On classifying sepsis heterogeneity in the ICU: insight using machine learning.

ABSTRACT:

Objectives

Current machine learning models aiming to predict sepsis from electronic health records (EHR) do not account 20 for the heterogeneity of the condition despite its emerging importance in prognosis and treatment. This work demonstrates the added value of stratifying the types of organ dysfunction observed in patients who develop sepsis in the intensive care unit (ICU) in improving the ability to recognize patients at risk of sepsis from their EHR data.

Materials and methods

Using an ICU dataset of 13 728 records, we identify clinically significant sepsis subpopulations with distinct organ dysfunction patterns. We perform classification experiments with random forest, gradient boost trees, and support vector machines, using the identified subpopulations to distinguis

SUBMITTER: Ibrahim ZM 

PROVIDER: S-EPMC7025363 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature