Crowdsourcing digital health measures to predict Parkinson's disease severity: the Parkinson's Disease Digital Biomarker DREAM Challenge
![Thumbnail](/xmlui/bitstream/handle/20.500.12573/1245/Crowdsourcing%20digital%20health%20measures%20to%20predict%20Parkinson%27s%20disease%20severity%20the%20Parkinson%27s%20Disease%20Digital%20Biomarker%20DREAM%20Challenge.pdf.jpg?sequence=4&isAllowed=y)
View/ Open
Access
info:eu-repo/semantics/openAccessDate
2021Author
Aydin, ZaferSieberts, Solveig K.
Schaff, Jennifer
Duda, Marlena
Pataki, Balint Armin
Sun, Ming
Snyder, Phil
Daneault, Jean-Francois
Parisi, Federico
Costante, Gianluca
Rubin, Udi
Banda, Peter
Chae, Yooree
Chaibub Neto, Elias
Dorsey, E. Ray
Chen, Aipeng
Elo, Laura L.
Espino, Carlos
Glaab, Enrico
Goan, Ethan
Golabchi, Fatemeh Noushin
Gormez, Yasin
Jaakkola, Maria K.
Jonnagaddala, Jitendra
Klen, Riku
Li, Dongmei
McDaniel, Christian
Perrin, Dimitri
Perumal, Thanneer M.
Rad, Nastaran Mohammadian
Rainaldi, Erin
Sapienza, Stefano
Schwab, Patrick
Shokhirev, Nikolai
Venalainen, Mikko S.
Vergara-Diaz, Gloria
Zhang, Yuqian
Wang, Yuanjia
Guan, Yuanfang
Brunner, Daniela
Bonato, Paolo
Mangravite, Lara M.
Omberg, Larsson
Metadata
Show full item recordAbstract
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).