Machine learning-based model for predicting 1 year mortality of hospitalized patients with heart failure.
Journal:
ESC heart failure
Published Date:
Aug 13, 2021
Abstract
AIMS: Individual risk stratification is a fundamental strategy in managing patients with heart failure (HF). Artificial intelligence, particularly machine learning (ML), can develop superior models for predicting the prognosis of HF patients, and administrative claim data (ACD) are suitable for ML analysis because ACD is a structured database. The objective of this study was to analyse ACD using an ML algorithm, predict the 1 year mortality of patients with HF, and finally develop an easy-to-use prediction model with high accuracy using the top predictors identified by the ML algorithm.