论文标题
希望 - 数据收集，监视和机器学习的集成数字表型平台
HOPES -- An Integrative Digital Phenotyping Platform for Data Collection, Monitoring and Machine Learning
论文作者
论文摘要
我们描述了全面的数字表型平台的发展和早期体验的发展：通过积极参与和自我授权的健康成果（希望）。希望基于开源Beiwe平台，但添加了更广泛的数据收集，包括将可穿戴数据源集成和智能手机的更多传感器收集。需求部分源自与精神分裂症的并发临床试验。该试验需要基于公共云和本地运营的仔细组合，开发出重要的能力，以期在安全，隐私，易用性和可扩展性方面发挥作用。我们描述了新的数据管道，以清洁，处理，呈现和分析数据。这包括根据研究操作和临床护理的需求定制的一组仪表板。通过分析SARS-COV-2大流行期间20名参与者的数字行为来描述希望的测试使用。
We describe the development of, and early experiences with, comprehensive Digital Phenotyping platform: Health Outcomes through Positive Engagement and Self-Empowerment (HOPES). HOPES is based on the open-source Beiwe platform but adds a much wider range of data collection, including the integration of wearable data sources and further sensor collection from the smartphone. Requirements were in part derived from a concurrent clinical trial for schizophrenia. This trial required development of significant capabilities in HOPES in security, privacy, ease-of-use and scalability, based on a careful combination of public cloud and on-premises operation. We describe new data pipelines to clean, process, present and analyze data. This includes a set of dashboards customized to the needs of the research study operations and for clinical care. A test use of HOPES is described by analyzing the digital behaviors of 20 participants during the SARS-CoV-2 pandemic.
