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

• URL: http://arxiv.org/abs/2402.13812v2
• Date: Wed, 14 Aug 2024 18:55:22 GMT
• Title: Voice-Driven Mortality Prediction in Hospitalized Heart Failure Patients: A Machine Learning Approach Enhanced with Diagnostic Biomarkers
• Authors: Nihat Ahmadli, Mehmet Ali Sarsil, Berk Mizrak, Kurtulus Karauzum, Ata Shaker, Erol Tulumen, Didar Mirzamidinov, Dilek Ural, Onur Ergen,
• Abstract summary: We demonstrate a powerful and effective Machine Learning model for predicting mortality rates in heart failure patients. By integrating voice biomarkers into routine patient monitoring, this strategy has the potential to improve patient outcomes. In this study, a Machine Learning system is trained to predict patients' 5-year mortality rates using their speech as input.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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 non-invasive and easily accessible means to evaluate patients' health. However, there is a lack of voice biomarkers for predicting mortality rates among heart failure patients with standardized speech protocols. Here, we demonstrate a powerful and effective ML model for predicting mortality rates in hospitalized HF patients through the utilization of voice biomarkers. By seamlessly integrating voice biomarkers into routine patient monitoring, this strategy has the potential to improve patient outcomes, optimize resource allocation, and advance patient-centered HF management. In this study, a Machine Learning system, specifically a logistic regression model, is trained to predict patients' 5-year mortality rates using their speech as input. The model performs admirably and consistently, as demonstrated by cross-validation and statistical approaches (p-value < 0.001). Furthermore, integrating NT-proBNP, a diagnostic biomarker in HF, improves the model's predictive accuracy substantially.

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