Neto_arXiv_2017_Dec
syn22017687
Created By
Meghasyam Tummalacherla meghasyam
url: https://arxiv.org/abs/1712.03120
Year: 2017
Study: mPower Mobile Parkinson Disease Study
Title: Learning Disease vs Participant Signatures: a permutation test approach to detect identity confounding in machine learning diagnostic applications
synID: syn4993293
Author: Neto EC Pratap A Perumal TM Tummalacherla M Bot BM Trister AD Friend SH Mangravite L Omberg L
Journal: arXiv
Abstract: syn22017687
Featured: TRUE
Diagnosis: Parkinson's disease control
Consortium: mHealth
sensorType: accelerometer gyroscope magnetometer touchscreen microphone
studyOrProject: [mPower Mobile Parkinson Disease Study](/Explore/Studies/DetailsPage?study=mPower%20Mobile%20Parkinson%20Disease%20Study)
publicationType: study
digitalAssessmentCategory: resting tremor kinetic tremor postural tremor action tremor gait voice
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