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.

翻译：心力衰竭作为一种全球性健康问题，其应对难度在于需采用创新方法改善患者护理。预测心衰患者的死亡率尤为困难但至关重要，这需要个体化护理、前瞻性管理，并支持基于充分信息的决策以改善预后。近年来，语音生物标志物结合机器学习（ML）的重要性显著提升，尤其在心力衰竭预测方面展现出卓越效能。语音分析与ML算法的协同作用提供了一种无创且易于获取的患者健康状况评估手段。然而，目前尚缺乏采用标准化语音协议预测心衰患者死亡率的语音生物标志物。本研究展示了一个基于语音生物标志物预测住院心衰患者死亡率的高效ML模型。通过将语音生物标志物无缝融入常规患者监测，该策略有望改善患者预后、优化资源配置，并推进以患者为中心的心衰管理。本研究采用机器学习系统（具体为逻辑回归模型），以患者的语音为输入，训练其预测患者5年死亡率。交叉验证与统计分析方法（p值<0.001）表明，该模型性能卓越且稳定。此外，整合心衰诊断生物标志物NT-proBNP可显著提升模型的预测准确性。