• Medientyp: E-Artikel
  • Titel: Crowdsourcing digital health measures to predict Parkinson’s disease severity: the Parkinson’s Disease Digital Biomarker DREAM Challenge
  • Beteiligte: 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; Aydın, Zafer; Chen, Aipeng; Elo, Laura L.; Espino, Carlos; Glaab, Enrico; Goan, Ethan; Golabchi, Fatemeh Noushin; Görmez, Yasin; Jaakkola, Maria K.; Jonnagaddala, Jitendra; [...]
  • Erschienen: Springer Science and Business Media LLC, 2021
  • Erschienen in: npj Digital Medicine
  • Sprache: Englisch
  • DOI: 10.1038/s41746-021-00414-7
  • ISSN: 2398-6352
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  • Anmerkungen:
  • Beschreibung: <jats:title>Abstract</jats:title><jats:p>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 <jats:italic>digital biomarkers</jats:italic> 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).</jats:p>
  • Zugangsstatus: Freier Zugang