Crowdsourcing digital health measures to predict Parkinson's disease severity: the Parkinson's Disea...
Crowdsourcing digital health measures to predict Parkinson's disease severity: the Parkinson's Disease Digital Biomarker DREAM Challenge
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Author / Creator
Sieberts, Solveig K , Schaff, Jennifer , Duda, Marlena , Pataki, Bálint Ármin , Sun, Ming , Snyder, Phil , Daneault, Jean-Francois , Parisi, Federico , Costante, Gianluca , Rubin, Udi , Banda, Peter , Chae, Yooree , Neto, Elias Chaibub , Dorsey, Ray , Aydın, Zafer , Chen, Aipeng , Elo, Laura L , Espino, Carlos , Glaab, Enrico , Goan, Ethan , Golabchi, Fatemeh Noushin , Yasin Görmez , Jaakkola, Maria K , Jonnagaddala, Jitendra , Klén, Riku , Li, Dongmei , Mcdaniel, Christian , Perrin, Dimitri , Nastaran Mohammadian Rad , Rainaldi, Erin , Sapienza, Stefano , Schwab, Patrick , Shokhirev, Nikolai , Venäläinen, Mikko S , Vergara-Diaz, Gloria , Zhang, Yuqian , Parkinson's Disease Digital Biomarker Challenge Consortium , Wang, Yuanjia , Guan, Yuanfang , Brunner, Daniela , Bonato, Paolo , Mangravite, Laura M and Larsson Omberg
Publisher
Cold Spring Harbor: Cold Spring Harbor Laboratory Press
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Language
English
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Publisher
Cold Spring Harbor: Cold Spring Harbor Laboratory Press
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More information
Scope and Contents
Contents
Mobile health, the collection of data using wearables and sensors, is a rapidly growing field in health research with many applications. Deriving validated measures of disease and severity that can be used clinically or as outcome measures in clinical trials, referred to as digital biomarkers, has proven difficult. In part due to the complicated analytical approaches necessary to develop these metrics. 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 Parkinson's Disease (PD) and severity of three PD symptoms: tremor, dyskinesia and bradykinesia. 40 teams from around the world submitted features, and achieved drastically improved predictive performance for PD (best AUROC=0.87), as well as severity of tremor (best AUPR=0.75), dyskinesia (best AUPR=0.48) and bradykinesia (best AUPR=0.95). Footnotes * https://www.synapse.org/DigitalBiomarkerChallenge...
Alternative Titles
Full title
Crowdsourcing digital health measures to predict Parkinson's disease severity: the Parkinson's Disease Digital Biomarker DREAM Challenge
Authors, Artists and Contributors
Author / Creator
Schaff, Jennifer
Duda, Marlena
Pataki, Bálint Ármin
Sun, Ming
Snyder, Phil
Daneault, Jean-Francois
Parisi, Federico
Costante, Gianluca
Rubin, Udi
Banda, Peter
Chae, Yooree
Neto, Elias Chaibub
Dorsey, Ray
Aydın, Zafer
Chen, Aipeng
Elo, Laura L
Espino, Carlos
Glaab, Enrico
Goan, Ethan
Golabchi, Fatemeh Noushin
Yasin Görmez
Jaakkola, Maria K
Jonnagaddala, Jitendra
Klén, Riku
Li, Dongmei
Mcdaniel, Christian
Perrin, Dimitri
Nastaran Mohammadian Rad
Rainaldi, Erin
Sapienza, Stefano
Schwab, Patrick
Shokhirev, Nikolai
Venäläinen, Mikko S
Vergara-Diaz, Gloria
Zhang, Yuqian
Parkinson's Disease Digital Biomarker Challenge Consortium
Wang, Yuanjia
Guan, Yuanfang
Brunner, Daniela
Bonato, Paolo
Mangravite, Laura M
Larsson Omberg
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Primary Identifiers
Record Identifier
TN_cdi_proquest_journals_2339463949
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2339463949
Other Identifiers
E-ISSN
2692-8205
DOI
10.1101/2020.01.13.904722
How to access this item
https://www.proquest.com/docview/2339463949?pq-origsite=primo&accountid=13902
