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Crowdsourcing digital health measures to predict Parkinson's disease severity: the Parkinson's Disease Digital Biomarker DREAM Challenge.
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; Chaibub Neto, Elias; Dorsey, E Ray; Aydin, Zafer; Chen, Aipeng; Elo, Laura L; Espino, Carlos; Glaab, Enrico; Goan, Ethan; Golabchi, Fatemeh Noushin; Görmez, Yasin; Jaakkola, Maria K; Jonnagaddala, Jitendra; Klén, Riku; Li, Dongmei; McDaniel, Christian; Perrin, Dimitri; Perumal, Thanneer M; Rad, Nastaran Mohammadian; Rainaldi, Erin; Sapienza, Stefano; Schwab, Patrick; Shokhirev, Nikolai; Venäläinen, Mikko S; Vergara-Diaz, Gloria; Zhang, Yuqian; Wang, Yuanjia; Guan, Yuanfang; Brunner, Daniela; Bonato, Paolo; Mangravite, Lara M; Omberg, Larsson.
Afiliação
  • Sieberts SK; Sage Bionetworks, Seattle, WA, USA. solly.sieberts@sagebase.org.
  • Schaff J; Elder Research, Inc, Charlottesville, VA, USA.
  • Duda M; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
  • Pataki BÁ; Department of Physics of Complex Systems, ELTE Eötvös Loránd University, Budapest, Hungary.
  • Sun M; Google Inc, New York, NY, USA.
  • Snyder P; Sage Bionetworks, Seattle, WA, USA.
  • Daneault JF; Dept of PM&R, Harvard Medical School, Spaulding Rehabilitation Hospital, Charlestown, MA, USA.
  • Parisi F; Dept of Rehabilitation and Movement Sciences, Rutgers University, Newark, NJ, USA.
  • Costante G; Dept of PM&R, Harvard Medical School, Spaulding Rehabilitation Hospital, Charlestown, MA, USA.
  • Rubin U; Wyss Institute, Harvard University, Boston, MA, USA.
  • Banda P; Dept of PM&R, Harvard Medical School, Spaulding Rehabilitation Hospital, Charlestown, MA, USA.
  • Chae Y; Wyss Institute, Harvard University, Boston, MA, USA.
  • Chaibub Neto E; Early Signal Foundation, 311 W 43rd Street, New York, NY, USA.
  • Dorsey ER; Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg.
  • Aydin Z; Sage Bionetworks, Seattle, WA, USA.
  • Chen A; Sage Bionetworks, Seattle, WA, USA.
  • Elo LL; Center for Health + Technology, University of Rochester, Rochester, NY, USA.
  • Espino C; Department of Electrical and Computer Engineering, Abdullah Gul University, Kayseri, Turkey.
  • Glaab E; Prince of Wales Clinical School, UNSW Sydney, Sydney, Australia.
  • Goan E; Turku Bioscience Centre, University of Turku and Åbo Akademi University, Tykistökatu 6, Turku, Finland.
  • Golabchi FN; Early Signal Foundation, 311 W 43rd Street, New York, NY, USA.
  • Görmez Y; Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg.
  • Jaakkola MK; School of Electrical Engineering and Robotics, Queensland University of Technology, Brisbane, QLD, Australia.
  • Jonnagaddala J; Dept of PM&R, Harvard Medical School, Spaulding Rehabilitation Hospital, Charlestown, MA, USA.
  • Klén R; Department of Electrical and Computer Engineering, Abdullah Gul University, Kayseri, Turkey.
  • Li D; Turku Bioscience Centre, University of Turku and Åbo Akademi University, Tykistökatu 6, Turku, Finland.
  • McDaniel C; Department of Mathematics and Statistics, University of Turku, Turku, Finland.
  • Perrin D; School of Public Health and Community Medicine, UNSW Sydney, Sydney, Australia.
  • Perumal TM; WHO Collaborating Centre for eHealth, UNSW Sydney, Sydney, Australia.
  • Rad NM; Turku Bioscience Centre, University of Turku and Åbo Akademi University, Tykistökatu 6, Turku, Finland.
  • Rainaldi E; Clinical and Translational Science Institute, University of Rochester Medical Center, Rochester, NY, USA.
  • Sapienza S; Artificial Intelligence, University of Georgia, Athens, GA, USA.
  • Schwab P; Computer Science, University of Georgia, Athens, GA, USA.
  • Shokhirev N; School of Computer Science, Queensland University of Technology, Brisbane, QLD, Australia.
  • Venäläinen MS; Sage Bionetworks, Seattle, WA, USA.
  • Vergara-Diaz G; Institute for Computing and Information Sciences, Radboud University, Nijmegen, The Netherlands.
  • Zhang Y; Fondazione Bruno Kessler (FBK), Via Sommarive 18, Povo, Trento, Italy.
  • Wang Y; Verily Life Sciences, 269 East Grand Ave, South San Francisco, CA, USA.
  • Guan Y; Dept of PM&R, Harvard Medical School, Spaulding Rehabilitation Hospital, Charlestown, MA, USA.
  • Brunner D; Institute of Robotics and Intelligent Systems, ETH Zurich, Zurich, Switzerland.
  • Bonato P; Early Signal Foundation, 311 W 43rd Street, New York, NY, USA.
  • Mangravite LM; Turku Bioscience Centre, University of Turku and Åbo Akademi University, Tykistökatu 6, Turku, Finland.
  • Omberg L; Dept of PM&R, Harvard Medical School, Spaulding Rehabilitation Hospital, Charlestown, MA, USA.
NPJ Digit Med ; 4(1): 53, 2021 Mar 19.
Article em En | MEDLINE | ID: mdl-33742069
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).
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Aspecto: Patient_preference Idioma: En Revista: NPJ Digit Med Ano de publicação: 2021 Tipo de documento: Article
Texto completo
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Aspecto: Patient_preference Idioma: En Revista: NPJ Digit Med Ano de publicação: 2021 Tipo de documento: Article