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.

通过整合语音生物标志物，我们展示了一个强大而有效的机器学习模型，用于通过语音评估预测住院心力衰竭患者的死亡率，该模型的预测精度得到增强。

基于机器学习方法和诊断生物标志物增强的住院心力衰竭患者声音驱动的死亡预测

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

Introduction: We present a screening method for early dementia using features
based on sound objects as voice biomarkers.
Methods: The final dataset used for machine learning models consisted of 266
observations, with a distribution of 186 healthy individuals, 46 diagnosed with
Alzheimer's, and 34 with MCI. This method is based on six-second recordings of
the sustained vowel /a/ spoken by the subject. The main original contribution
of this work is the use of carefully crafted features based on sound objects.
This approach allows one to first represent the sound spectrum in a more
accurate way than the standard spectrum, and then build interpretable features
containing relevant information about subjects' control over their voice.
Results: ROC AUC obtained in this work for distinguishing healthy subjects
from those with MCI was 0.85, while accuracy was 0.76. For distinguishing
between healthy subjects and those with either MCI or Alzheimer's the results
were 0.84, 0.77, respectively.
Conclusion: The use of features based on sound objects enables screening for
early dementia even on very short recordings of language-independent voice
samples.

基于声音对象的特征作为声音生物标志物，提出了一种早期认知功能障碍筛查方法。通过使用机器学习模型对包括声音对象的精心构造特征进行分析，可以对语言无关的声音样本进行短时筛查。