Saez-Pons, J. and Stamate, C. and Weston, D. and Roussos, George (2019) PDkit: an open source data science toolkit for Parkinson's disease. In: UbiComp/ISWC '19 Adjunct, 9-13 September 2019, London, UK.
Abstract
Parkinson's Disease (PD) is a long-term neurodegenerative disorder that affects over four million people worldwide. State-of-the-art mobile and wearable sensing technologies offer the prospect of enhanced clinical care pathways for PD patients through integration of automated symptom tracking within current healthcare infrastructures. Yet, even though sensor data collection can be performed efficiently today using these technologies, automated inference of high-level severity scores from such data is still limited by the lack of validated evidence, despite a plethora of published research. In this paper, we introduce PDkit, an open source toolkit for PD progression monitoring using multimodal sensor data obtained by smartphone apps or wearables. We discuss how PDkit implements an information processing pipeline incorporating distinct stages for data ingestion and quality assessment, feature and biomarker estimation, and clinical scoring using high-level clinical scales. Finally, we demonstrate how PDkit facilitates outcome reproducibility and algorithmic transparency in the CUSSP clinical trial, a pilot, dual-site, open label study.
Metadata
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Additional Information: | Proceedings ISBN: 9781450368698 |
| School: | Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences |
| Depositing User: | George Roussos |
| Date Deposited: | 09 May 2022 19:26 |
| Last Modified: | 09 Aug 2023 12:52 |
| URI: | https://eprints.bbk.ac.uk/id/eprint/47090 |
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