Article (Scientific journals)
Crowdsourcing digital health measures to predict Parkinson's disease severity: the Parkinson's Disease Digital Biomarker DREAM Challenge
Sieberts, S.; Schaff, J.; Duda, M. et al.
2021In npj Digital Medicine, 4 (53)
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Keywords :
Parkinson’s Disease; Digital Biomarker; machine learning; tremor; dyskinesia; bradykinesia; mobile phone; smart sensors; biomarkers
Abstract :
[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).
Research center :
- Luxembourg Centre for Systems Biomedicine (LCSB): Biomedical Data Science (Glaab Group)
Disciplines :
Human health sciences: Multidisciplinary, general & others
Life sciences: Multidisciplinary, general & others
Author, co-author :
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.
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External co-authors :
Language :
Title :
Crowdsourcing digital health measures to predict Parkinson's disease severity: the Parkinson's Disease Digital Biomarker DREAM Challenge
Publication date :
Journal title :
npj Digital Medicine
Publisher :
Nature Publishing Group, United States
Volume :
Issue :
Peer reviewed :
Peer Reviewed verified by ORBi
Focus Area :
Systems Biomedicine
Additional URL :
FnR Project :
FNR11651464 - Multi-dimensional Stratification Of Parkinson'S Disease Patients For Personalised Interventions, 2017 (01/07/2018-30/06/2021) - Enrico Glaab
Name of the research project :
PD-Strat > Multi-dimensional Stratification Of Parkinson’S Disease Patients For Personalised Interventions > 01/07/2018 > 30/06/2021 > 2017
Funders :
FNR - Fonds National de la Recherche [LU]
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since 08 February 2021


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