![](https://d3ilqtpdwi981i.cloudfront.net/UVl0WExwKHAhIR-IUjPOC2pTAXU=/425x550/smart/https://bepress-attached-resources.s3.amazonaws.com/uploads/7c/66/02/7c660207-b77e-434d-9ba1-80a639516c23/thumbnail_def55caa-8e1d-4a8e-b716-070b189b78a6.jpg)
Unpublished Paper
Predicting Parkinson's Disease Progression with Smartphone Data
Kno.e.sis Publications
Document Type
Report
Publication Date
3-1-2013
Disciplines
Abstract
Most of the existing approaches for detecting diseases/risk score form observations (sensor and textual) ignore the presence of any prior knowledge of the disease. In this work, we start top-down by enumerating the symptoms of Parkinson's Disease (PD) and map the symptoms to its possible manifestations in sensor observations (bottom-up). We show such manifestations and further use these manifestations as features to build classifiers to differentiate between the PD patients and the control group.
Citation Information
Pramod Anantharam, Krishnaprasad Thirunarayan, Vahid Taslimi and Amit P. Sheth. "Predicting Parkinson's Disease Progression with Smartphone Data" (2013) Available at: http://works.bepress.com/tk_prasad/2/
Submitted to the Parkinson's disease challenge sponsored by The Michael J. Fox Foundation for Parkinson's Research, March, 2013.