SCH: Personalized Depression Treatment Support by Mobile Sensor Analytics

SCH：移动传感器分析提供的个性化抑郁症治疗支持

• 批准号: 10196980
• 负责人: Jinbo Bi
• 金额: $ 24.56万
• 依托单位: UNIVERSITY OF CONNECTICUT STORRS
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-18 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10196980
• 关键词: Adult; Algorithms; Caring; Cellular Phone; Clinical; Clinical Data; Communities; Data; Data Sources; Drops; Early treatment; Ecological momentary assessment; Family; Formulation; Health Technology; Influentials; Learning; Location; Machine Learning; Mental Depression; Mental Health; Methods; Modeling; Noise; Participant; Patient Monitoring; Patient Self-Report; Patients; Pharmaceutical Preparations; Phase; Physicians; Practice Guidelines; Prediction of Response to Therapy; Process; Provider; Public Health; Questionnaires; Research; Safety; Sampling; Self Administration; Sensory; Sleep; Symptoms; System; Techniques; Time; Treatment outcome; autoencoder; base; clinical decision-making; data de-identification; deep learning; denoising; depressive symptoms; design; feature selection; global health; heterogenous data; innovation; mHealth; medication compliance; medication safety; multimodality; multitask; novel; predictive modeling; recruit; response; sensor; symptomatology; tool; treatment response; user-friendly

The current best practice guidelines for treating depression call for close monitoring of patients, and periodically adjusting treatment as needed. This project will advance personalized depression treatment by developing an innovative system, DepWatch, that leverages mobile health technologies and machine learning tools to provide clinicians objective, accurate, and timely assessment of depression symptoms to assist with their clinical decision making process. Specifically, DepWatch collects sensory data passively from smartphones and wristbands, without any user interaction, and uses simple user-friendly interfaces to collect ecological momentary assessments (EMA), medication adherence and safety related data from patients. The collected data will be fed to machine learning models to be developed in the project to provide weekly assessment of patient symptom levels and predict the trajectory of treatment response over time. The assessment and prediction results are then presented using a graphic interface to clinicians to help them make critical treatment decisions. Our project comprises two studies. Phase I collects sensory data and other data (e.g., clinical data, EMA, tolerability and safety data) from 250 adult participants with unstable depression symptomatology. The data thus collected will be used to develop and validate assessment and prediction models, which will be incorporated into DepWatch system. In Phase II, three clinicians will use DepWatch to support their clinical decision making process; a total of 50 participants under treatment by the three participating clinicians will be recruited for the study. A number of innovative machine learning techniques will be developed. These include a set of new learning formulations to construct matrix-based longitudinal predictive models, and determine the temporal contingency and the most influential features, and deep learning based data imputation methods that can handle both problems of sporadic missing data as well as missing data in an entire view. In addition, multi-task feature learning models and feature selection techniques will be expanded and refined for this challenging setting of large-scale heterogeneous data.

治疗抑郁症的当前最佳实践指南要求对患者进行密切监测，并 根据需要定期调整治疗。该项目将通过 开发一种创新的系统Depwatch，该系统利用移动健康技术和机器 学习工具以提供临床医生的客观，准确和及时评估抑郁症状 协助他们的临床决策过程。具体而言，DepWatch被动地收集感官数据 来自智能手机和腕带，没有任何用户交互，并使用简单的用户友好接口到 收集生态瞬时评估（EMA），从 患者。收集到的数据将被馈送到要在项目中开发的机器学习模型 每周评估患者症状水平，并预测治疗反应的轨迹 时间。然后，使用临床医生的图形界面来提出评估和预测结果 帮助他们做出关键的治疗决策。我们的项目包括两项研究。第一阶段收集感官 来自250名成年参与者的数据和其他数据（例如临床数据，EMA，耐受性和安全数据） 不稳定的抑郁症状学。因此收集的数据将用于开发和验证 评估和预测模型，该模型将纳入DepWatch系统。在第二阶段，三个 临床医生将使用Depwatch来支持其临床决策过程；共有50名参与者 在三名参与的临床医生的治疗下，将招募该研究。许多创新 将开发机器学习技术。这些包括一组新的学习配方 构建基于基质的纵向预测模型，并确定时间偶然性和 大多数有影响力的功能以及可以解决这两个问题的基于深度学习的数据插补方法 在整个视图中零星丢失的数据以及丢失的数据。此外，多任务功能学习 对于这种挑战的大规模设置，模型和功能选择技术将被扩展和完善 异质数据。