Developing better digital health measures of Parkinson's disease using free living data and a crowdsourced data analysis challenge
| Autor: | 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, Jayaraman, Arun, Evers, Luc J. W., Foschini, Luca, Mariakakis, Alex, Pandey, Gaurav, Shawen, Nicholas, Synder, Phil, Omberg, Larsson |
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| Přispěvatelé: | BEAT-PD, DREAM Challenge Consortium [collaborator], Fonds National de la Recherche - FnR [sponsor], Luxembourg Centre for Systems Biomedicine (LCSB): Biomedical Data Science (Glaab Group) [research center] |
| Jazyk: | angličtina |
| Rok vydání: | 2023 |
| Předmět: |
Multidisciplinaire
généralités & autres [F99] [Sciences du vivant] Neurologie [D14] [Sciences de la santé humaine] Multidisciplinaire généralités & autres [D99] [Sciences de la santé humaine] Biotechnologie [F06] [Sciences du vivant] Neurology [D14] [Human health sciences] Biotechnology [F06] [Life sciences] Multidisciplinary general & others [F99] [Life sciences] Multidisciplinary general & others [D99] [Human health sciences] |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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