Voice-Driven Mortality Prediction in Hospitalized Heart Failure
Patients: A Machine Learning Approach Enhanced with Diagnostic Biomarkers
- URL: http://arxiv.org/abs/2402.13812v1
- Date: Wed, 21 Feb 2024 13:50:46 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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