MHealth Monitoring of Acoustic and Behavioral Patterns in Bipolar Disorder Across Cultures

摘要： 根据言语中的行为和声音模式对个人的医疗保健进行优先排序的能力将允许有效地使用医疗保健资源。通过移动技术衍生的日常声学监测来预测情绪状态的能力，为情绪和情感状态的实时替代测量提供了基础。对人类疾病的这些和其他方面特征的识别和监测是预测结果的基础，为今后及时和减轻干预提供了可能性。由于方法的灵活性和适应性，技术和移动保健方法非常适合全球卫生界；通过适度增加基础设施，可以很容易地扩大接触到大量患者的能力。我们使用机器学习策略开发了双相(BP)患者情绪状态的准确预测模型，并建立了一个过程，包括预处理、特征提取和对个人使用移动设备长达一年所收集的临床和声学数据的综合数据分析。结果表明，情绪状态预测的AUC统计量为0.74(躁狂)和0.77(抑郁)。我们假设，跨文化的分析将确定使用我们的方法可以识别的疾病的共同特征。BP是研究的理想对象，因为它具有广泛的情绪状态和气质特征。这项研究的目的是：1)确定来自黎巴嫩和密歇根东南部一个多语言社区的30名BP患者和10名健康对照，使用智能手机记录日常声学和行为数据，收集设备发出的所有语音，并记录所有个人数字活动。我们建议对黎巴嫩和密歇根东南部的参与者进行研究，以确定双相情感障碍患者情绪变化的基本声学因素。2)使用静态(高斯混合模型和支持向量机)和动态(隐马尔可夫模型)对临床、声音和行为信号的分类、维度和派生特征进行建模，应用集成的计算分析；我们将比较来自黎巴嫩的15个BP和来自密歇根东南部的15个BP的数据，这些人在美国居住了两年，但在语言和文化上来自与黎巴嫩相当的地理区域，以及15名美国出生的BP高加索人(来自我们当前的队列)。我们的假设是，无论文化如何，声学中都有与情绪状态相关的基本要素。其影响是纵向使用移动技术被动收集个人数据，以建立计算模型，使用大量的个人状态和特征数据来准确预测情绪和健康状态。这为预测性建模提供了基础，可以集成到后续的临床干预研究中，以预测和测试特定干预措施对疾病机制的因果影响。临床、计算和技术领域的专业知识组成了实现这些目标的团队。

Abstract: The ability to prioritize individuals for health care based on behavioral and acoustic patterns in speech will allow for efficient use of health care resources. The ability to predict mood states using daily monitoring of acoustics derived from mobile technology provides the basis for a real-time proxy measure of moods and affective states. Identification and monitoring of these and other dimensional features of human disease is the base for anticipating outcomes, offering the future possibility of timely and mitigating interventions. Technological and mHealth methods are well suited for the global health community due to the flexibility and adaptability of the approach; the capacity to reach large numbers of patients can be easily amplified with modest increase in infrastructure. We have developed an accurate prediction model for mood states in bipolar (BP) individuals using machine-learning strategies and established a process that involves preprocessing, feature extraction, and an integrated data analysis of clinical and acoustic data gathered from personal use of a mobile device for up to one year. The results show mood states are predicted with an AUC statistic of 0.74 (mania) and 0.77 (depression). We hypothesize that analyses across cultures will identify common features of illness that can be identified using our methods. BP is ideal for study because of the wide range of mood states and temperamental traits. This study aims to 1) ascertain 30 individuals with BP and 10 healthy controls from Lebanon and a multilingual community in SE Michigan, recording daily acoustic and behavioral data using a smart-phone, all outgoing speech from the device is gathered and all personal digital activity is recorded from the device. We propose to study participants in Lebanon and SE Michigan in order to identify the fundamental acoustic elements of mood variation among bipolar patients. 2) apply integrated computational analyses using static (Gaussian Mixture Models and Support Vector Machines) and dynamic (Hidden Markov Models) modeling of categorical, dimensional and derived features from clinical, acoustic, and behavioral signals; we will compare data from the 15 BP from Lebanon and 15 BP from SE Michigan that have been resident in USA >2 years but originate from a geographical region comparable to Lebanon in language and culture, and 15 American born BP Caucasians (from our current cohort). Our hypothesis is that there are fundamental elements of acoustics that associate with mood states regardless of the culture. The impact is the longitudinal use of mobile technology to passively gather personal data to establish computational models that use extensive individual state and trait data to accurately predict mood and health states. This provides a foundation for predictive modeling that can be integrated into subsequent clinical interventional studies to predict and test causal effects of specific interventions on disease mechanisms. Expertise in clinical, computational, and technology disciplines form the team to realize these goals.