SCH: Personalized Depression Treatment Support by Mobile Sensor Analytics

目前治疗抑郁症的最佳实践指南要求对患者进行密切监测， 根据需要定期调整治疗。该项目将推进个性化抑郁症治疗， 开发一个创新的系统，DepWatch，利用移动的健康技术和机器 学习工具，为临床医生提供客观，准确和及时的抑郁症症状评估， 协助他们的临床决策过程。具体来说，DepWatch被动地收集传感数据 从智能手机和腕带，没有任何用户交互，并使用简单的用户友好的界面， 收集生态瞬时评估（EMA）、药物依从性和安全性相关数据， 患者收集到的数据将被输入到项目中开发的机器学习模型中， 提供每周一次的患者症状水平评估，并预测治疗反应的轨迹 时间评估和预测结果然后使用图形界面呈现给临床医生， 帮助他们做出关键的治疗决定。我们的项目包括两项研究。第一阶段收集感官 数据和其它数据（例如，临床数据、EMA、耐受性和安全性数据）， 不稳定性抑郁症由此收集的数据将用于开发和验证 评估和预测模型，将纳入DepWatch系统。在第二阶段，3 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 将招募正在接受三名参与临床医生治疗的患者进行研究。一批创新 将开发机器学习技术。其中包括一套新的学习公式， 构建基于矩阵的纵向预测模型，并确定时间偶然性和 最具影响力的特征，以及基于深度学习的数据填补方法，可以处理这两个问题 零星的缺失数据以及整个视图中的缺失数据。此外，多任务特征学习 模型和特征选择技术将被扩展和完善，以适应这种具有挑战性的大规模环境。 异构数据

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.