SBIR Phase II: Augmenting Virtual Healthcare with Voice Biomarkers

SBIR 第二阶段：利用语音生物标记增强虚拟医疗保健

• 批准号: 2036213
• 负责人: Grace Chang
• 金额: $ 100万
• 依托单位: Kintsugi Mindful Wellness, Inc.
• 依托单位国家: 美国
• 项目类别: Cooperative Agreement
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-15 至 2025-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2036213&HistoricalAwards=false
• 关键词: SBIR; Phase; II; Augmenting; Virtual

The broader impact of this Small Business Innovation Research (SBIR) Phase II project is to develop smart, robust healthcare infrastructure in the U.S. by leveraging machine learning and artificial intelligence to streamline clinical decision support. Voice biomarkers detect a variety of health conditions, emotions, and diseases and provide a unique, seamless feedback for real-time triage. Transforming voice intonations into voice biomarkers would allow disease prediction and monitoring. The proposed voice biomarker technology is potentially a scalable behavioral health screener to provide equitable care in all virtual care visits, mitigating the complex and costly (2-3X) comorbidities of depression and anxiety in 80% of $3T in chronic conditions. This Small Business Innovation Research (SBIR) Phase II project is dedicated to providing scalable mental health screening in primary care. The research objectives are to understand the underlying behavioral health triggers for chronic health conditions from global voice biomarker data combined with unique, longitudinal metadata. The major technical challenges in this proposed research include (1) collecting sufficiently diverse metadata labels on environmental and physiological variables,(2) training distinct models based on gender, age, and other features that have high variance through principal component analysis, (3) identifying and minimizing bias for sparse populations in design, validation, and deployment phases, and (4) improving the current voice biomarker diagnostic on dimensions of sensitivity, specificity, and diagnosability in various call center, telehealth platform, remote patient monitoring, and care management platform modalities. The highly complex deployments across infrastructure in healthcare require multiple models tuned for specific health populations and a deep understanding of classical and deep learning techniques for improving both accuracy and generalizability across unseen populations. The anticipated technical results in solving this series of highly challenging machine learning tasks is profound for real-time triage and access to reliable mental healthcare at scale.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这个小型企业创新研究（SBIR）第二阶段项目的更广泛影响是通过利用机器学习和人工智能来简化临床决策支持，在美国开发智能，强大的医疗保健基础设施。语音生物标志物可以检测各种健康状况、情绪和疾病，并为实时分类提供独特、无缝的反馈。将语音语调转换为语音生物标志物将有助于疾病预测和监测。拟议的语音生物标志物技术可能是一种可扩展的行为健康筛选器，可以在所有虚拟护理访问中提供公平的护理，减轻3 T美元慢性疾病中80%的抑郁症和焦虑症的复杂和昂贵（2- 3倍）合并症。这个小企业创新研究（SBIR）第二阶段项目致力于在初级保健中提供可扩展的心理健康筛查。研究目标是从全球语音生物标志物数据结合独特的纵向元数据中了解慢性健康状况的潜在行为健康触发因素。这项拟议研究的主要技术挑战包括（1）收集关于环境和生理变量的足够多样化的元数据标签，（2）通过主成分分析基于性别，年龄和其他具有高方差的特征训练不同的模型，（3）在设计，验证和部署阶段识别并最大限度地减少稀疏人群的偏见，以及（4）在各种呼叫中心、远程医疗平台、远程患者监视和护理管理平台模态中，在灵敏度、特异性和可诊断性的维度上改进当前的语音生物标记诊断。医疗保健基础设施中高度复杂的部署需要针对特定健康人群调整多个模型，并深入了解经典和深度学习技术，以提高未知人群的准确性和可推广性。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。