Crowdsourcing digital health measures to predict Parkinson's disease severity: the Parkinson's Disease Digital Biomarker DREAM Challenge
- Sieberts, S.
- Schaff, J.
- Duda, M.
- Pataki, B.
- Sun, M.
- Snyder, P.
- Daneault, J.
- Parisi, F.
- Costante, G.
- Rubin, U.
- Banda, P.
- Chae, Y.
- Neto, E.
- Dorsey, E.
- Aydin, Z.
- Chen, A.
- Elo, L.
- Espino, C.
- Glaab, Enrico
- Goan, E.
- Golabchi, F.
- Görmez, Y.
- Jaakkola, M.
- Jonnagaddala, J.
- Klén, R.
- Li, D.
- McDaniel, C.
- Perrin, D.
- Rad, N.
- Perumal, T.
- Rainaldi, E.
- Sapienza, S.
- Schwab, P.
- Shokhirev, N.
- Venäläinen, M.
- Vergara-Diaz, G.
- Wang, Y.
- Consortium, The Parkinson S Disease Digital Biomarker Challenge
- Guan, Y.
- Brunner, D.
- Bonato, P.
- Mangravite, L.
- Omberg, L.
DOIAbstract
peer reviewedConsumer wearables and sensors are a rich source of data about patients’ daily disease and symptom burden, particularly in the case of movement disorders like Parkinson’s Disease (PD). However, interpreting these complex data into so-called digital biomarkers requires complicated analytical approaches, and validating these biomarkers requires sufficient data and unbiased evaluation methods. Here we describe the use of crowdsourcing to specifically evaluate and benchmark features derived from accelerometer and gyroscope data in two different datasets to predict the presence of PD and severity of three PD symptoms: tremor, dyskinesia and bradykinesia. Forty teams from around the world submitted features, and achieved drast...
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