Interpretable Machine Learning for Early Prediction of Prognosis in Sepsis: A Discovery and Validation Study.

ABSTRACT:

Introduction

This study aimed to develop and validate an interpretable machine-learning model based on clinical features for early predicting in-hospital mortality in critically ill patients with sepsis.

Methods

We enrolled all patients with sepsis in the Medical Information Mart for Intensive Care IV (MIMIC-IV, v.1.0) database from 2008 to 2019. Lasso regression was used for feature selection. Seven machine-learning methods were applied to develop the models. The best model was selected based on its accuracy and area under curve (AUC) in the validation cohort. Furthermore, we employed the SHapley Additive exPlanations (SHAP) method to illustrate the effects of the features attributed to the model, and to analyze how the individual features affect the output of the model, an

SUBMITTER: Hu C 

PROVIDER: S-EPMC9124279 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature