What drives performance in machine learning models for predicting heart failure outcome?

ABSTRACT:

Aims

The development of acute heart failure (AHF) is a critical decision point in the natural history of the disease and carries a dismal prognosis. The lack of appropriate risk-stratification tools at hospital discharge of AHF patients significantly limits clinical ability to precisely tailor patient-specific therapeutic regimen at this pivotal juncture. Machine learning-based strategies may improve risk stratification by incorporating analysis of high-dimensional patient data with multiple covariates and novel prediction methodologies. In the current study, we aimed at evaluating the drivers for success in prediction models and establishing an institute-tailored artificial Intelligence-based prediction model for real-time decision support.

Methods and results

We used a coh

SUBMITTER: Gutman R 

PROVIDER: S-EPMC10232285 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature