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Crowdsourcing digital health measures to predict Parkinson's disease severity: the Parkinson's Disease Digital Biomarker DREAM Challenge

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Item Type:Article
Title:Crowdsourcing digital health measures to predict Parkinson's disease severity: the Parkinson's Disease Digital Biomarker DREAM Challenge
Creators Name:Sieberts, S.K., Schaff, J., Duda, M., Pataki, B.Á., Sun, M., Snyder, P., Daneault, J.F., Parisi, F., Costante, G., Rubin, U., Banda, P., Chae, Y., Chaibub Neto, E., Dorsey, E.R., Aydın, Z., Chen, A., Elo, L.L., Espino, C., Glaab, E., Goan, E., Golabchi, F.N., Görmez, Y., Jaakkola, M.K., Jonnagaddala, J., Klén, R., Li, D., McDaniel, C., Perrin, D., Perumal, T.M., Rad, N.M., Rainaldi, E., Sapienza, S., Schwab, P., Shokhirev, N., Venäläinen, M.S., Vergara-Diaz, G., Zhang, Y., Wang, Y., Guan, Y., Brunner, D., Bonato, P., Mangravite, L.M. and Omberg, L.
Abstract:Consumer 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 drastically improved predictive performance for PD status (best AUROC = 0.87), as well as tremor- (best AUPR = 0.75), dyskinesia- (best AUPR = 0.48) and bradykinesia-severity (best AUPR = 0.95).
Source:NPJ Digital Medicine
ISSN:2398-6352
Publisher:Springer
Volume:4
Number:1
Page Range:53
Date:19 March 2021
Additional Information:Wolfgang Kopp is a member of the Parkinson’s Disease Digital Biomarker Challenge Consortium.
Official Publication:https://doi.org/10.1038/s41746-021-00414-7
PubMed:View item in PubMed

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