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密切监测患者，以及 根据需要定期调整治疗。该项目将通过以下方式推进个性化抑郁症治疗 开发利用移动医疗技术和机器的创新系统DepWatch 为临床医生提供客观、准确和及时的抑郁症状评估的学习工具 协助他们的临床决策过程。具体地说，DepWatch被动地收集感觉数据 智能手机和腕带，无需任何用户交互，并使用简单的用户友好界面 收集生态瞬时评估(EMA)、用药依从性和安全相关数据 病人。收集到的数据将被馈送到机器学习模型中，以便在项目中开发 每周评估患者的症状水平，并预测治疗反应的轨迹 时间到了。然后使用图形界面将评估和预测结果呈现给临床医生 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 来自250名成年参与者的数据和其他数据(例如，临床数据、EMA、耐受性和安全性数据) 不稳定的抑郁症状学。收集的数据将用于开发和验证 评估和预测模型，这些模型将被纳入DepWatch系统。在第二阶段，第三阶段 临床医生将使用DepWatch支持他们的临床决策过程；总共有50名参与者 在三名参与的临床医生的治疗下，将为这项研究招募人员。一些创新的 将开发机器学习技术。其中包括一套新的学习公式，以 构建基于矩阵的纵向预测模型，并确定时间偶然性和 最有影响力的特征，以及可以处理这两个问题的基于深度学习的数据填充方法 零星的丢失数据以及整个视图中的丢失数据。此外，多任务特征学习 模型和特征选择技术将针对这一具有挑战性的大规模环境进行扩展和改进 异类数据。

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.