用语音分析提前筛查老年人糖尿病，准确率超七成。
A Voice-based Triage for Type 2 Diabetes using a Conversational Virtual Assistant in the Home Environment
- 从对话语音中提取7个非识别特征，实现轻量化筛查
- 男性受试者筛查准确率达70%，女性60%
- 可部署在家庭虚拟助手设备中，适合老年慢病管理
将云计算与物联网技术结合，近年来在泛在医疗领域取得显著进展。其中，基于语音的病理分析尚未受到学术界和产业界的足够关注。利用语音分析进行重大疾病的早期检测，有望显著改善患者健康状况与生活质量。本文提出一种新型声学机器学习分诊系统，集成于大众化对话式虚拟助手，用于在家环境中预筛2型糖尿病。我们采集了24名老年人与虚拟助手对话时的语音，提取声学特征并预测糖尿病发病情况。系统对男性和女性老年受试者的命中率分别为70%和60%。所提方法仅依赖7个非识别语音特征，可在资源受限的嵌入式系统中运行。该应用证明了语音病理分析在家庭环境中实现慢性病早期检测的可行性，有助于提升老年人健康水平。
原文摘要 · Abstract (English)
Incorporating cloud technology with Internet of Medical Things for ubiquitous healthcare has seen many successful applications in the last decade with the advent of machine learning and deep learning techniques. One of these applications, namely voice-based pathology, has yet to receive notable attention from academia and industry. Applying voice analysis to early detection of fatal diseases holds much promise to improve health outcomes and quality of life of patients. In this paper, we propose a novel application of acoustic machine learning based triaging into commoditised conversational virtual assistant systems to pre-screen for onset of diabetes. Specifically, we developed a triaging system which extracts acoustic features from the voices of n=24 older adults when they converse with a virtual assistant and predict the incidence of Diabetes Mellitus (Type 2) or not. Our triaging system achieved hit-rates of 70% and 60% for male and female older adult subjects, respectively. Our proposed triaging uses 7 non-identifiable voice-based features and can operate within resource-constrained embedded systems running voice-based virtual assistants. This application demonstrates the feasibility of applying voice-based pathology analysis to improve health outcomes of older adults within the home environment by early detection of life-changing chronic conditions like diabetes.
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