Machine learning algorithm to predict the in-hospital mortality in critically ill patients with chronic kidney disease.

ABSTRACT:

Background

This study aimed to establish and validate a machine learning (ML) model for predicting in-hospital mortality in critically ill patients with chronic kidney disease (CKD).

Methods

This study collected data on CKD patients from 2008 to 2019 using the Medical Information Mart for Intensive Care IV. Six ML approaches were used to build the model. Accuracy and area under the curve (AUC) were used to choose the best model. In addition, the best model was interpreted using SHapley Additive exPlanations (SHAP) values.

Results

There were 8527 CKD patients eligible for participation; the median age was 75.1 (interquartile range: 65.0-83.5) years, and 61.7% (5259/8527) were male. We developed six ML models with clinical variables as input factors. Among the six model

SUBMITTER: Li X 

PROVIDER: S-EPMC10201999 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature