Voice-Driven Mortality Prediction in Hospitalized Heart Failure Patients: A Machine Learning Approach Enhanced with Diagnostic Biomarkers

arXiv:2402.13812v1 Announce Type: new
Abstract: Addressing heart failure (HF) as a prevalent global health concern poses difficulties in implementing innovative approaches for enhanced patient care. Predicting mortality rates in HF patients, in particular, is difficult yet critical, necessitating individualized care, proactive management, and enabling educated decision-making to enhance outcomes. Recently, the significance of voice biomarkers coupled with Machine Learning (ML) has surged, demonstrating remarkable efficacy, particularly in predicting heart failure. The synergy of voice analysis and ML algorithms provides a …

abstract arxiv cs.lg cs.sd diagnostic eess.as enabling failure global global health health heart failure machine machine learning management mortality patient patient care patients prediction type voice