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

PLoS Comput Biol. 2021 Mar 12;17(3):e1008833. doi: 10.1371/journal.pcbi.1008833. eCollection 2021 Mar.

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Data Science*
  • Diagnosis, Computer-Assisted / methods*
  • Humans
  • Mobile Applications
  • Parkinson Disease / diagnosis*
  • Smartphone
  • Software*

Grants and funding

GR, DW, CS and JSP acknowledge support by the Michael J. Fox Foundation for Parkinson’s Research (MJFF) with Grant ID 14781 to GR & DW for the project entitled “A Scalable Computational Data Science Toolbox for High-Frequency Assessment of PD” awarded under its Computational Science 2017 programme. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.