Machine learning enhanced acute heart failure phenotype prediction using natural language processing and random forest.

ABSTRACT:

Background

Heart failure (HF), with its distinct phenotypes, poses significant public health challenges. Early diagnosis of specific HF phenotypes is crucial for timely therapeutic intervention.

Objectives

We employed random forests to predict acute HF (AHF) phenotypes (HFrEF, HFmrEF, and HFpEF) during admission, using structured and unstructured data types while blinded to left ventricular ejection fraction (LVEF) information.

Methods

We investigated the predictive performance of integrated natural language processing (NLP) and machine learning (ML)-based models in AHF phenotype classification by random forests, leveraging clinical text and laboratory data from the MIMIC-III database. Feature selection for unstructured textual data and biochemical test data was perfo

SUBMITTER: Chang PH 

PROVIDER: S-EPMC12571787 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature