Construction and validation of prognostic model for ICU mortality in cardiac arrest patients: an interpretable machine learning modeling approach.

ABSTRACT:

Background

The incidence and mortality of cardiac arrest (CA) is high. We developed interpretable machine learning models for early prediction of ICU mortality risk in patients diagnosed with CA.

Methods

Data from the Medical Information Mart for Intensive Care (MIMIC-IV, version 2.2) was randomized to training set (0.7) and internal validation set (0.3), and data from eICU(version 2.0.1) was used as external validation set. Five models including Logistic Regression (LR), Random Forest (RF), K Nearest Neighbor (KNN), Decision Tree (DT), and Extreme Gradient Boost (XGBoost) were developed. The model with the largest area under the Receiver Operating Characteristic (ROC) curve (AUC) and good performance in other features was defined as the best model, and Shapley Additive Expl

SUBMITTER: Li Y 

PROVIDER: S-EPMC12020013 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature