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

• Published in: PLoS computational biology Vol. 17; no. 3; p. e1008833
• Main Authors: Stamate, Cosmin, Saez Pons, Joan, Weston, David, Roussos, George
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 12.03.2021; Public Library of Science (PLoS)
• Subjects: Algorithms; Bias; Biology and Life Sciences; Clinical trials; Computational biology; Computer and Information Sciences; Computer applications; Computer programs; Data processing; Data science; Design; Diagnosis; Digitization; Drug therapy; Engineering and Technology; Feature selection; Information processing; Learning algorithms; Machine learning; Medicine and Health Sciences; Methods; Model testing; Monitoring; Motivation; Movement disorders; Neurodegenerative diseases; Open source software; Parkinson's disease; Patients; Phenotypes; Physical therapy; Public domain; Public software; Research and Analysis Methods; Science Policy; Sensors; Signs and symptoms; Smartphones; Software; Software development; Surgery; Symptom management; Telemedicine; Toolkits; United Kingdom
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1008833

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.