Building a Next Generation Model for Biomedical Research: Validation of Health Sensors using Online Community Registries and Collaborative Data Interpretation
Abstract
The current siloed approach to biomedical research is ill equipped to take advantage of the emerging tools to query biological systems and remarkable data now available. Our original proposal was for a pilot study to assess the feasibility of using a community-based collaborative approach to doing research, engaging individuals with Parkinsons disease, and monitoring diseases using voice pattern recognition. This project establishes a scientific challenge to optimize the analysis of self-reported outcome data and sustained voice phonation recordings of individuals from the PatientsLikeMe Parkinsons disease online patient community. The main goal of the project is to establish through collaborative data interpretation whether non-lab quality voice recordings such as web-based, cell-phone or home phone recordings could function as valid health sensors that can be used to predict patient function and disease severity. The relationship between aspects of voice such as pitch, volume and timbre, and characteristics of Parkinsons disease such as tremor or slowness, might be complex and multifactorial. Therefore we invited cross-disciplinary experts to develop algorithms to determine disease severity from these data. The underlying goal was to assess the therapeutic value of voice analysis. Another goal for this project was to identify the barriers and solutions to expand the scope of this work to broader community engagement, longitudinal measurements, smartphone integration and modeling of therapeutic drug effects. This was a feasibility study, intended to precede the design of a larger-scale project.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jul 31, 2017
- Accession Number
- AD1080662
Entities
People
- Stephen H. Friend
Organizations
- Sage Bionetworks