Explainable machine learning and online calculators to predict heart failure mortality in intensive care units.

ABSTRACT:

Aims

This study aims to develop explainable machine learning models and clinical tools for predicting mortality in patients in the intensive care unit (ICU) with heart failure (HF).

Methods

Patients diagnosed with HF who experienced their first ICU stay lasting between 24 h and 28 days were selected from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. The primary outcome was all-cause mortality within 28 days. Data analysis was performed using Python and R, with feature selection conducted via least absolute shrinkage and selection operator (LASSO) regression. Fifteen models were evaluated, and the most effective model was rendered explainable through the Shapley additive explanations (SHAP) approach. A nomogram was developed based on logistic regress

SUBMITTER: Chen AT 

PROVIDER: S-EPMC11769656 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature