Clinical Utility of Machine Learning-Derived Vocal Biomarkers in the Management of Heart Failure.

ABSTRACT:

Background

This study aimed to systematically evaluate voice symptoms during heart failure (HF) treatments and to exploratorily extract HF-related vocal biomarkers.

Methods and results

This single-center, prospective study longitudinally acquired 839 audio files from 59 patients with acute decompensated HF. Patients' voices were analyzed along with conventional HF indicators (New York Heart Association [NYHA] class, presence of pulmonary congestion and pleural effusion on chest X-ray, and B-type natriuretic peptide [BNP]) and GOKAN scores based on the assessment of a cardiologist. Machine-learning (ML) models to estimate HF conditions were created using a Light Gradient Boosting Machine. Voice analysis identified 27 acoustic features that correlated with conventional HF indi

SUBMITTER: Okada K 

PROVIDER: S-EPMC11309773 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature