SCH: Digital Biomarker and Analytics for Cognitive Impairment with Mobile and Wearable Sensing

阿尔茨海默病和相关痴呆症（ADRD）是一种日益增长的流行病，在缺乏有效的治疗方法的情况下， 治疗方面，随着人口老龄化，疾病负担增加。在ADRD和轻度认知障碍中 (MCI)，疾病进展存在显著的时间变异性，增加了管理的难度 患者的舒适和安全。早期发现症状状态和持续监测被视为 作为有效的措施，以尽量减少疾病的影响，因为各种形式的干预可以提供 治疗、补偿和应对的机会。然而，目前临床认知和 行为评估有许多缺点，它们在很大程度上是非定量的， 通常很难确定是否有显着的变化，在神经状况之间， 探访此外，评估不经常获得，并且不能客观地说明 日常活动中可能出现的疾病相关行为。在这个项目中，我们建议 新的计算方法和分析，以确定用于ADRD检测、预测 在诊所外进行监控这种技术驱动的方法被动地基于传感器数据 从商品智能手机和可穿戴设备中获得，并为新型嵌入式 通过持续监测评估认知状态。该提案提出了几项研究 机会首先，我们将使用多模态推进被动和连续数据收集方法 感应我们将解决的挑战包括优化电池使用以实现长期数据捕获，以及 通过执行设备上的数据和特征预处理来减轻隐私问题。其次，我们将 建立在行为和上下文识别，语音分析和 机器学习来识别阿尔茨海默病和相关疾病的数字生物标志物。我们将 利用这些生物标志物来构建疾病阶段表征的计算模型， 预测，并通过将种族和民族风险因素作为先验来个性化它们。最后他们希望 促进这些模型和数字生物标志物在临床实践中的使用，我们将提出一种新的视觉 分析界面，帮助医生和健康从业者与所获得的传感器进行交互 数据，验证数字生物标志物，验证模型结果，并预测疾病的进展。 相关性（参见说明）： 一个明确和具体的临床需求激发了这一提议：改进和持续的理解， 监测、表征、评估和预测流行的神经认知状况， 自然主义的设置。ADRD是一种治疗困难且昂贵的疾病，影响着美国数百万人 一个人我们的方法为早期检测和预测这种疾病的新方向提供了基础。 毁灭性和高度衰弱的状况。

Alzheimer's disease and related dementias (ADRD) is a growing epidemic, and in the absence of effective treatment, disease burden increases as the population ages. In both ADRD and mild cognitive impairment (MCI), there is significant temporal variability in disease progression, increasing the difficulty for managing patient comfort and safety. Early detection of symptomatic states and continuous monitoring are regarded as effective measures to minimize the impact of the disease as various forms of intervention can provide opportunities for treatment, compensation and coping. However, current clinic-based cognitive and behavioral assessments have numerous shortcomings; they are largely non-quantitative and clinicians often have difficulty determining if there has been significant changes in neurologic condition between visits. Additionally, assessments are obtained infrequently, and do not objectively account for disease-related behaviors that could be revealed in daily activities. In this project, we propose to advance new computational approaches and analytics to identify digital biomarkers for ADRD detection, prediction and monitoring outside the clinic. This technology-driven approach is based on sensor data passively acquired from commodity smartphones and wearables, and provides the foundation for a novel embedded assessment of cognitive status through continuous monitoring.This proposal presents several research opportunities. Firstly, we will advance passive and continuous data collection methods using multimodal sensing. Challenges we will address include optimizing battery use for long-term data capture, and mitigating privacy concerns by performing on-device data and feature pre-processing. Secondly, we will be building on state-of-the-art research techniques in behavior and context recognition, speech analysis, and machine learning to identify digital biomarkers of Alzheimer's disease and related disorders. We will leverage these biomarkers to build computational models for disease stage characterization and prediction, and individualize them by incorporating race and ethnicity risk factors as priors. Lastly, to facilitate the use of these models and digital biomarkers in clinical practice, we will advance a novel visual analytics interface towards helping physicians and health practitioners interact with the acquired sensor data, validate the digital biomarkers, verify model results, and forecast the progression of disease. RELEVANCE (See instructions): A clear and specific clinical need motivates this proposal: improved and continuous understanding, monitoring, characterization, assessment and prediction of a prevalent neuro-cognitive condition in naturalistic settings. ADRDs are difficult and costly diseases to treat, affecting millions of people in the U.S alone. Our approach provides the foundation for a new direction in the early detection and prediction of this devastating and highly-debilitating condition.