Abstract
PDkit is an open source software toolkit supporting the collaborative development of novel methods of digital assessment for Parkinson's Disease, using symptom measurements captured continuously by wearables (passive monitoring) or by high-use-frequency smartphone apps (active monitoring). The goal of the toolkit is to help address the current lack of algorithmic and model transparency in this area by facilitating open sharing of standardised methods that allow the comparison of results across multiple centres and hardware variations. PDkit adopts the information-processing pipeline abstraction incorporating stages for data ingestion, quality of information augmentation, feature extraction, biomarker estimation and finally, scoring using standard clinical scales. Additionally, a dataflow programming framework is provided to support high performance computations. The practical use of PDkit is demonstrated in the context of the CUSSP clinical trial in the UK. The toolkit is implemented in the python programming language, the de facto standard for modern data science applications, and is widely available under the MIT license.
Highlights
Parkinson’s is the second most common neurodegenerative disease across the globe with as many as 10 million patients worldwide
A key motivation in the development of PDkit is the recognition that digital assessments of motor severity could significantly improve the sensitivity of clinical trials and personalise treatment in Parkinson’s Disease (PD) but face considerable challenges before they can be widely adopted
The primary outcome was the degree to which subject-level smartphone-based measures calculated using a study-specific PDkit pipeline predicted subject-level Part III MDS-UPDRS subitems as assessed by three blinded clinical raters. This was quantified as the leave-one-subject-out cross-validation (LOSO-CV) predictive accuracy of a range of features and machine learning algorithms implemented using PDkit
Summary
Peer Review History: PLOS recognizes the benefits of transparency in the peer review process; we enable the publication of all of the content of peer review and author responses alongside final, published articles. Research in new treatments are limited because the clinical tools used to assess its symptoms are subjective, require considerable time to perform and specialised skills and can only detect coarse-grain changes. To address this situation, clinicians are turning to smartphone apps and wearables to create new ways to assess symptoms that are more sensitive to change and can be applied frequently at home by patients and their carers. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript This is a PLOS Computational Biology Software paper
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