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|Title:||HOPES : an integrative digital phenotyping platform for data collection, monitoring, and machine learning||Authors:||Wang, Xuancong
Lee, Jimmy Chee Keong
Morris, Robert J. T.
|Issue Date:||2021||Source:||Wang, X., Vouk, N., Heaukulani, C., Buddhika, T., Martanto, W., Lee, J. C. K. & Morris, R. J. T. (2021). HOPES : an integrative digital phenotyping platform for data collection, monitoring, and machine learning. Journal of Medical Internet Research, 23(3), e23984-. https://dx.doi.org/10.2196/23984||Journal:||Journal of Medical Internet Research||Abstract:||The collection of data from a personal digital device to characterize current health conditions and behaviors that determine how an individual's health will evolve has been called digital phenotyping. In this paper, we describe the development of and early experiences with a 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 wider range of data collection, including the integration of wearable devices and further sensor collection from smartphones. Requirements were partly derived from a concurrent clinical trial for schizophrenia that required the development of significant capabilities in HOPES for 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 research study operations and clinical care. A test use case for HOPES was described by analyzing the digital behavior of 22 participants during the SARS-CoV-2 pandemic.||URI:||https://hdl.handle.net/10356/149218||ISSN:||1438-8871||DOI:||10.2196/23984||Rights:||© Xuancong Wang, Nikola Vouk, Creighton Heaukulani, Thisum Buddhika, Wijaya Martanto, Jimmy Lee, Robert JT Morris. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 15.03.2021. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||LKCMedicine Journal Articles|
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Updated on Sep 29, 2022
Updated on Sep 29, 2022
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