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

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, View ORCID ProfileJitendra 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
doi: https://doi.org/10.1101/2020.01.13.904722
Solveig K. Sieberts
1Sage Bionetworks, Seattle, WA 98121
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  • For correspondence: solly.sieberts@sagebionetworks.org larsson.omberg@sagebionetworks.org
Jennifer Schaff
2Elder Research, Inc., Charlottesville, VA, 22903
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Marlena Duda
3Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109
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Bálint Ármin Pataki
4Department of Physics of Complex Systems, ELTE Eötvös Loránd University, Budapest, Hungary
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Ming Sun
5Google Inc, New York, NY, USA 10011
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Phil Snyder
1Sage Bionetworks, Seattle, WA 98121
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Jean-Francois Daneault
6Dept of PM&R, Harvard Medical School, Spaulding Rehabilitation Hospital, Charlestown, MA, 02129
7Dept of Rehabilitation and Movement Sciences, Rutgers University, Newark, NJ 07107
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Federico Parisi
6Dept of PM&R, Harvard Medical School, Spaulding Rehabilitation Hospital, Charlestown, MA, 02129
8Wyss Institute, Harvard University, Boston, MA, 02115
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Gianluca Costante
6Dept of PM&R, Harvard Medical School, Spaulding Rehabilitation Hospital, Charlestown, MA, 02129
8Wyss Institute, Harvard University, Boston, MA, 02115
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Udi Rubin
9Early Signal Foundation, 311 W 43rd Street, New York, NY 10036
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Peter Banda
10Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, L-4362, Luxembourg
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Yooree Chae
1Sage Bionetworks, Seattle, WA 98121
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Elias Chaibub Neto
1Sage Bionetworks, Seattle, WA 98121
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E. Ray Dorsey
11Center for Health + Technology, University of Rochester, Rochester, NY 14642
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Zafer Aydın
12Department of Electrical and Computer Engineering, Abdullah Gul University, Kayseri, Turkey
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Aipeng Chen
13Prince of Wales Clinical School, UNSW Sydney, Australia
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Laura L. Elo
14Turku Bioscience Centre, University of Turku and Åbo Akademi University, Tykistökatu 6, FI-20520 Turku, Finland
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Carlos Espino
9Early Signal Foundation, 311 W 43rd Street, New York, NY 10036
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Enrico Glaab
10Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, L-4362, Luxembourg
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Ethan Goan
15School of Electrical Engineering and Robotics, Queensland University of Technology, Brisbane, Queensland, Australia, 4000
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Fatemeh Noushin Golabchi
6Dept of PM&R, Harvard Medical School, Spaulding Rehabilitation Hospital, Charlestown, MA, 02129
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Yasin Görmez
12Department of Electrical and Computer Engineering, Abdullah Gul University, Kayseri, Turkey
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Maria K. Jaakkola
14Turku Bioscience Centre, University of Turku and Åbo Akademi University, Tykistökatu 6, FI-20520 Turku, Finland
16Department of Mathematics and Statistics, University of Turku, FI-20014 Turku, Finland
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Jitendra Jonnagaddala
17School of Public Health and Community Medicine, UNSW Sydney, Australia
18WHO Collaborating Centre for eHealth, UNSW Sydney, Australia
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  • ORCID record for Jitendra Jonnagaddala
Riku Klén
14Turku Bioscience Centre, University of Turku and Åbo Akademi University, Tykistökatu 6, FI-20520 Turku, Finland
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Dongmei Li
19Clinical and Translational Science Institute, University of Rochester Medical Center, Rochester, NY, USA, 14642
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Christian McDaniel
20Artificial Intelligence, University of Georgia, Athens, GA, USA, 30602
21Computer Science, University of Georgia, Athens, GA, USA, 30602
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Dimitri Perrin
22School of Computer Science, Queensland University of Technology, Brisbane, Queensland, Australia, 4000
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Thanneer M. Perumal
1Sage Bionetworks, Seattle, WA 98121
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Nastaran Mohammadian Rad
23Institute for Computing and Information Sciences, Radboud University, Nijmegen, The Netherlands, 6525EC
24Fondazione Bruno Kessler (FBK), Via Sommarive 18, 38123, Povo, Trento, Italy
25University of Trento, Italy, 38122 TN
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Erin Rainaldi
26Verily Life Sciences, 269 East Grand Ave, South San Francisco, CA 94080
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Stefano Sapienza
6Dept of PM&R, Harvard Medical School, Spaulding Rehabilitation Hospital, Charlestown, MA, 02129
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Patrick Schwab
27Institute of Robotics and Intelligent Systems, ETH Zurich, Zurich, Switzerland, CH-8092
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Nikolai Shokhirev
9Early Signal Foundation, 311 W 43rd Street, New York, NY 10036
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Mikko S. Venäläinen
14Turku Bioscience Centre, University of Turku and Åbo Akademi University, Tykistökatu 6, FI-20520 Turku, Finland
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Gloria Vergara-Diaz
6Dept of PM&R, Harvard Medical School, Spaulding Rehabilitation Hospital, Charlestown, MA, 02129
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Yuqian Zhang
28School of Biomedical Engineering, Shanghai Jiao Tong University, China
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Yuanjia Wang
29Department of Biostatistics, Mailman School of Public Health, Columbia University, 722 W168th Street, New York, NY 10032
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Yuanfang Guan
3Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109
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Daniela Brunner
9Early Signal Foundation, 311 W 43rd Street, New York, NY 10036
30Dept. of Psychiatry, Columbia University, New York, NY
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Paolo Bonato
6Dept of PM&R, Harvard Medical School, Spaulding Rehabilitation Hospital, Charlestown, MA, 02129
8Wyss Institute, Harvard University, Boston, MA, 02115
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Lara M. Mangravite
1Sage Bionetworks, Seattle, WA 98121
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Larsson Omberg
1Sage Bionetworks, Seattle, WA 98121
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  • For correspondence: solly.sieberts@sagebionetworks.org larsson.omberg@sagebionetworks.org
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Abstract

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. 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 (best AUPR=0.95) severity.

Footnotes

  • https://www.synapse.org/DigitalBiomarkerChallenge

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
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Posted February 20, 2020.
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Crowdsourcing digital health measures to predict Parkinson’s disease severity: the Parkinson’s Disease Digital Biomarker DREAM Challenge
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
bioRxiv 2020.01.13.904722; doi: https://doi.org/10.1101/2020.01.13.904722
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Crowdsourcing digital health measures to predict Parkinson’s disease severity: the Parkinson’s Disease Digital Biomarker DREAM Challenge
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
bioRxiv 2020.01.13.904722; doi: https://doi.org/10.1101/2020.01.13.904722

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