PDKit: a data science toolkit for the digital assessment of Parkinson's Disease

Stamate, Cosmin and Saez Pons, Joan and Weston, David and Roussos, George (2021) PDKit: a data science toolkit for the digital assessment of Parkinson's Disease. PLoS Computational Biology 17 (3), e1008833. ISSN 1553-7358.

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.

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