Reference : Crowdsourcing digital health measures to predict Parkinson's disease severity: the Pa...
Scientific journals : Article
Life sciences : Biotechnology
Life sciences : Multidisciplinary, general & others
Human health sciences : Neurology
Human health sciences : Multidisciplinary, general & others
Systems Biomedicine
http://hdl.handle.net/10993/46140
Crowdsourcing digital health measures to predict Parkinson's disease severity: the Parkinson's Disease Digital Biomarker DREAM Challenge
English
Sieberts, S. [> >]
Schaff, J. [> >]
Duda, M. [> >]
Pataki, B. [> >]
Sun, M. [> >]
Snyder, P. [> >]
Daneault, J. [> >]
Parisi, F. [> >]
Costante, G. [> >]
Rubin, U. [> >]
Banda, P. [> >]
Chae, Y. [> >]
Neto, E. [> >]
Dorsey, E. [> >]
Aydin, Z. [> >]
Chen, A. [> >]
Elo, L. [> >]
Espino, C. [> >]
Glaab, Enrico mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Biomedical Data Science]
Goan, E. [> >]
Golabchi, F. [> >]
Görmez, Y. [> >]
Jaakkola, M. [> >]
Jonnagaddala, J. [> >]
Klén, R. [> >]
Li, D. [> >]
McDaniel, C. [> >]
Perrin, D. [> >]
Rad, N. [> >]
Perumal, T. [> >]
Rainaldi, E. [> >]
Sapienza, S. [> >]
Schwab, P. [> >]
Shokhirev, N. [> >]
Venäläinen, M. [> >]
Vergara-Diaz, G. [> >]
Wang, Y. [University of Luxembourg > Faculty of Humanities, Education and Social Sciences (FHSE) > Department of Humanities (DHUM)]
Consortium, The Parkinson S Disease Digital Biomarker Challenge [> >]
Guan, Y. [> >]
Brunner, D. [> >]
Bonato, P. [> >]
Mangravite, L. [> >]
Omberg, L. [> >]
2021
npj Digital Medicine
Nature Publishing Group
4
53
Yes (verified by ORBilu)
International
2398-6352
United States
[en] Parkinson’s Disease ; Digital Biomarker ; machine learning ; tremor ; dyskinesia ; bradykinesia ; mobile phone ; smart sensors ; biomarkers
[en] 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).
Luxembourg Centre for Systems Biomedicine (LCSB): Biomedical Data Science (Glaab Group)
Fonds National de la Recherche - FnR
PD-Strat > Multi-dimensional Stratification Of Parkinson’S Disease Patients For Personalised Interventions > 01/07/2018 > 30/06/2021 > 2017
Researchers ; Professionals ; Students
http://hdl.handle.net/10993/46140
10.1038/s41746-021-00414-7
https://rdcu.be/cg4tS
The original publication is available at: https://doi.org/10.1038/s41746-021-00414-7
FnR ; FNR11651464 > Enrico Glaab > PD-Strat > Multi-dimensional Stratification Of Parkinson’S Disease Patients For Personalised Interventions > 01/07/2018 > 30/06/2021 > 2017

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