| Reference : Developing better digital health measures of Parkinson's disease using free living da... |
| Scientific journals : Article | |||
| Life sciences : Biotechnology Life sciences : Multidisciplinary, general & others Human health sciences : Neurology Human health sciences : Multidisciplinary, general & others | |||
| Systems Biomedicine; Computational Sciences | |||
| http://hdl.handle.net/10993/54729 | |||
| Developing better digital health measures of Parkinson's disease using free living data and a crowdsourced data analysis challenge. | |
| English | |
| Sieberts, Solveig K. [> >] | |
| Borzymowski, Henryk [> >] | |
| Guan, Yuanfang [> >] | |
| Huang, Yidi [> >] | |
| Matzner, Ayala [> >] | |
| Page, Alex [> >] | |
| Bar-Gad, Izhar [> >] | |
| Beaulieu-Jones, Brett [> >] | |
| El-Hanani, Yuval [> >] | |
| Goschenhofer, Jann [> >] | |
| Javidnia, Monica [> >] | |
| Keller, Mark S. [> >] | |
| Li, Yan-Chak [> >] | |
| Saqib, Mohammed [> >] | |
| Smith, Greta [> >] | |
| Stanescu, Ana [> >] | |
| Venuto, Charles S. [> >] | |
| Zielinski, Robert [> >] | |
Glaab, Enrico  [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Biomedical Data Science] | |
| Jayaraman, Arun [> >] | |
| Evers, Luc J. W. [> >] | |
| Foschini, Luca [> >] | |
| Mariakakis, Alex [> >] | |
| Pandey, Gaurav [> >] | |
| Shawen, Nicholas [> >] | |
| Synder, Phil [> >] | |
| Omberg, Larsson [> >] | |
| BEAT-PD, DREAM Challenge Consortium [> >] | |
| 2023 | |
| PLoS Digital Health | |
| 2 | |
| 3 | |
| e0000208 | |
| Yes | |
| International | |
| [en] One of the promising opportunities of digital health is its potential to lead to more holistic understandings of diseases by interacting with the daily life of patients and through the collection of large amounts of real-world data. Validating and benchmarking indicators of disease severity in the home setting is difficult, however, given the large number of confounders present in the real world and the challenges in collecting ground truth data in the home. Here we leverage two datasets collected from patients with Parkinson's disease, which couples continuous wrist-worn accelerometer data with frequent symptom reports in the home setting, to develop digital biomarkers of symptom severity. Using these data, we performed a public benchmarking challenge in which participants were asked to build measures of severity across 3 symptoms (on/off medication, dyskinesia, and tremor). 42 teams participated and performance was improved over baseline models for each subchallenge. Additional ensemble modeling across submissions further improved performance, and the top models validated in a subset of patients whose symptoms were observed and rated by trained clinicians. | |
| Luxembourg Centre for Systems Biomedicine (LCSB): Biomedical Data Science (Glaab Group) | |
| Fonds National de la Recherche - FnR | |
| DIGIPD > Validating Digital Biomarkers For Better Personalized Treatment Of Parkinson’S Disease > 01/05/2021 > 30/04/2024 > 2020 | |
| Researchers ; Professionals ; Students | |
| http://hdl.handle.net/10993/54729 | |
| 10.1371/journal.pdig.0000208 | |
| https://doi.org/10.1371/journal.pdig.0000208 | |
| The original publication is available at https://doi.org/10.1371/journal.pdig.0000208 | |
| Copyright: © 2023 Sieberts et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. | |
| FnR ; FNR14599012 > Enrico Glaab > DIGIPD > Validating Digital Biomarkers For Better Personalized Treatment Of Parkinson’S Disease > 01/05/2021 > 30/04/2024 > 2020 |
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