| 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  [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 |
| File(s) associated to this reference | ||||||||||||||
|
Fulltext file(s):
| ||||||||||||||
All documents in ORBilu are protected by a user license.
