Application of Machine Learning for Clinical Subphenotype Identification in Sepsis.

ABSTRACT:

Introduction

Sepsis is a heterogeneous clinical syndrome. Identification of sepsis subphenotypes could lead to allowing more precise therapy. However, there is a lack of models to identify the subphenotypes in such patients. Thus, we aimed to identify possible subphenotypes and compare the clinical outcomes for subphenotypes in a large sepsis cohort.

Methods

This machine learning-based, cluster analysis was performed using the Medical Information Mart in Intensive Care (MIMIC)-IV database. We enrolled all adult (> 18 years old) patients diagnosed with sepsis in the first 24 h after intensive care unit (ICU) admission. K-means cluster analysis was performed to identify the number of classes. Multivariable logistic regression models were used to estimate the association betwee

SUBMITTER: Hu C 

PROVIDER: S-EPMC9617989 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature