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...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	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
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/45ee48713e9b4dd591d3f8cc81d31894
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Sea el primero en dejar un comentario!

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

Ejemplares similares