Development of interpretable machine learning models to predict in-hospital prognosis of acute heart failure patients.

ABSTRACT:

Aims

In recent years, there has been remarkable development in machine learning (ML) models, showing a trend towards high prediction performance. ML models with high prediction performance often become structurally complex and are frequently perceived as black boxes, hindering intuitive interpretation of the prediction results. We aimed to develop ML models with high prediction performance, interpretability, and superior risk stratification to predict in-hospital mortality and worsening heart failure (WHF) in patients with acute heart failure (AHF).

Methods and results

Based on the Kyoto Congestive Heart Failure registry, which enrolled 4056 patients with AHF, we developed prediction models for in-hospital mortality and WHF using information obtained on the first day of admi

SUBMITTER: Tanaka M 

PROVIDER: S-EPMC11424291 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature