Author: Wang, Xuancong; Vouk, Nikola; Heaukulani, Creighton; Buddhika, Thisum; Martanto, Wijaya; Lee, Jimmy; Morris, Robert JT
                    Title: HOPES: An Integrative Digital Phenotyping Platform for Data Collection, Monitoring, and Machine Learning  Cord-id: he1n4afs  Document date: 2021_3_15
                    ID: he1n4afs
                    
                    Snippet: 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 th
                    
                    
                    
                     
                    
                    
                    
                    
                        
                            
                                Document: 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.
 
  Search related documents: 
                                Co phrase  search for related documents- location sleep and lockdown effect: 1
  - lockdown behavior and machine learning: 1
  - lockdown effect and machine learning: 1, 2, 3, 4, 5, 6, 7, 8
  - lockdown effect and machine learning approach: 1
  - long distance and machine learning: 1
  - low maintenance and machine learning: 1
  - low maintenance and machine learning approach: 1
  
 
                                Co phrase  search for related documents, hyperlinks ordered by date