Using machine learning algorithms to predict 28-day mortality in critically ill elderly patients with colorectal cancer.

ABSTRACT:

Objective

To predict the 28-day mortality of critically ill, elderly patients with colorectal cancer (CRC) using five machine learning approaches.

Methods

Data were extracted from the eICU Collaborative Research Database (eICU-CRD) (version 2.0) for a training cohort and from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) and Wuhan Union hospital for validation cohorts. Clinical information (i.e., demographics; initial laboratory tests; vital signs; outcomes) were collected. Five machine learning algorithms (LightGBM, decision tree, XGBoost, random forest, and ensemble model) and a logistic regression were applied for the prediction of 28-day mortality.

Results

Overall, 693 patients were included from the eICU cohort, 181 patients from the MIMIC-IV coho

SUBMITTER: Guo C 

PROVIDER: S-EPMC10640810 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature