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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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
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Computer applications to medicine. Medical informatics
R858-859.7
Acceso en línea:https://doaj.org/article/45ee48713e9b4dd591d3f8cc81d31894
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spelling oai:doaj.org-article:45ee48713e9b4dd591d3f8cc81d318942021-12-02T16:31:03ZCrowdsourcing digital health measures to predict Parkinson’s disease severity: the Parkinson’s Disease Digital Biomarker DREAM Challenge10.1038/s41746-021-00414-72398-6352https://doaj.org/article/45ee48713e9b4dd591d3f8cc81d318942021-03-01T00:00:00Zhttps://doi.org/10.1038/s41746-021-00414-7https://doaj.org/toc/2398-6352Abstract 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).Solveig K. SiebertsJennifer SchaffMarlena DudaBálint Ármin PatakiMing SunPhil SnyderJean-Francois DaneaultFederico ParisiGianluca CostanteUdi RubinPeter BandaYooree ChaeElias Chaibub NetoE. Ray DorseyZafer AydınAipeng ChenLaura L. EloCarlos EspinoEnrico GlaabEthan GoanFatemeh Noushin GolabchiYasin GörmezMaria K. JaakkolaJitendra JonnagaddalaRiku KlénDongmei LiChristian McDanielDimitri PerrinThanneer M. PerumalNastaran Mohammadian RadErin RainaldiStefano SapienzaPatrick SchwabNikolai ShokhirevMikko S. VenäläinenGloria Vergara-DiazYuqian Zhangthe Parkinson’s Disease Digital Biomarker Challenge ConsortiumYuanjia WangYuanfang GuanDaniela BrunnerPaolo BonatoLara M. MangraviteLarsson OmbergNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 4, Iss 1, Pp 1-12 (2021)
institution DOAJ
collection DOAJ
language EN
topic Computer applications to medicine. Medical informatics
R858-859.7
spellingShingle Computer applications to medicine. Medical informatics
R858-859.7
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
Crowdsourcing digital health measures to predict Parkinson’s disease severity: the Parkinson’s Disease Digital Biomarker DREAM Challenge
description 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 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).
format article
author 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
author_facet 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
author_sort Solveig K. Sieberts
title Crowdsourcing digital health measures to predict Parkinson’s disease severity: the Parkinson’s Disease Digital Biomarker DREAM Challenge
title_short Crowdsourcing digital health measures to predict Parkinson’s disease severity: the Parkinson’s Disease Digital Biomarker DREAM Challenge
title_full Crowdsourcing digital health measures to predict Parkinson’s disease severity: the Parkinson’s Disease Digital Biomarker DREAM Challenge
title_fullStr Crowdsourcing digital health measures to predict Parkinson’s disease severity: the Parkinson’s Disease Digital Biomarker DREAM Challenge
title_full_unstemmed Crowdsourcing digital health measures to predict Parkinson’s disease severity: the Parkinson’s Disease Digital Biomarker DREAM Challenge
title_sort crowdsourcing digital health measures to predict parkinson’s disease severity: the parkinson’s disease digital biomarker dream challenge
publisher Nature Portfolio
publishDate 2021
url https://doaj.org/article/45ee48713e9b4dd591d3f8cc81d31894
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