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

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

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...

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Auteurs principaux: Solveig K. Sieberts, Jennifer Schaff, Marlena Duda, Bálint Ármin Pataki, Ming Sun, Phil Snyder, Jean-Francois Daneault, Federico Parisi, Gianluca Costante, Udi Rubin, Peter Banda, Yooree Chae, Elias Chaibub Neto, E. Ray Dorsey, Zafer Aydın, Aipeng Chen, Laura L. Elo, Carlos Espino, Enrico Glaab, Ethan Goan, Fatemeh Noushin Golabchi, Yasin Görmez, Maria K. Jaakkola, Jitendra Jonnagaddala, Riku Klén, Dongmei Li, Christian McDaniel, Dimitri Perrin, Thanneer M. Perumal, Nastaran Mohammadian Rad, Erin Rainaldi, Stefano Sapienza, Patrick Schwab, Nikolai Shokhirev, Mikko S. Venäläinen, Gloria Vergara-Diaz, Yuqian Zhang, the Parkinson’s Disease Digital Biomarker Challenge Consortium, Yuanjia Wang, Yuanfang Guan, Daniela Brunner, Paolo Bonato, Lara M. Mangravite, Larsson Omberg
Format: article
Langue:EN
Publié: Nature Portfolio 2021
Sujets:
Computer applications to medicine. Medical informatics
R858-859.7
Accès en ligne:https://doaj.org/article/45ee48713e9b4dd591d3f8cc81d31894
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https://doaj.org/article/45ee48713e9b4dd591d3f8cc81d31894

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