PDKit: A data science toolkit for the digital assessment of Parkinson’s Disease
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...
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| Published in | PLoS computational biology Vol. 17; no. 3; p. e1008833 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Public Library of Science
12.03.2021
Public Library of Science (PLoS) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1553-7358 1553-734X 1553-7358 |
| DOI | 10.1371/journal.pcbi.1008833 |
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| 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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| AbstractList | 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. 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. Parkinson’s Disease is the fastest growing neurological condition affecting millions of people across the world. People with Parkinson’s suffer from a variety of symptoms that result in diminished ability to move, eat, remember or sleep. 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. In this paper, we discuss PDkit, an open source toolkit that we developed to help address this current lack of algorithmic and model transparency. Adopting PDkit facilitates the open sharing of standardised methods and can accelerate the development of new methods and system to assess Parkinson’s and enables research groups to innovate. The toolkit provides funcionality that support data ingestion, quality of information augmentation, feature extraction, biomarker estimation and finally, scoring using standard clinical scales. The practical use of PDkit is demonstrated via its use by the CUSSP clinical trial conducted in the UK. Since there is currently no cure, clinical care pathways for PD are focused on symptom management, a life-long process that typically includes pharmacological treatment, physiotherapy and, at the advanced stages of the disease, surgery [3]. PD studies have often failed to account for inter-rater variability leading to machine learning models also learning subjective bias. [...]the highly heterogeneous presentation of PD cannot be fully captured by monitoring only a small subset of symptoms thus resulting in too blunt an instrument failing to capture critical symptoms and hence being unable to define a deep phenotype at the individual level. [...]it reduces the costs of bespoke software development in DHT-based exploratory research and clinical studies, especially those related to software implementation and verification. * It enhances confidence in the computational outcomes produced in studies, due to the fact that the software is tested by a large user community. * It provides concise, domain-specific programming abstractions specifically targeting Parkinson’s, thus eliminating the need to write repetitive code. * Contrary to proprietary software, it provides the ability to inspect the algorithms employed and their implementation, thus facilitating the in-depth exploration of the results generated and of any clinical inferences made. The motivation for adopting this approach is our desire to balance the need for a low barrier of entry for developers so as to encourage the adoption of this toolkit; and at the same time, to cater to modern scalable information processing architectures, which are critical in deploying digital assessments at population scale. [...]the combination of an information pipeline approach and an adaptable programming model enables PDkit to effectively support both active and passive monitoring within an integrated framework. 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.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. Since there is currently no cure, clinical care pathways for PD are focused on symptom management, a life-long process that typically includes pharmacological treatment, physiotherapy and, at the advanced stages of the disease, surgery [3]. PD studies have often failed to account for inter-rater variability leading to machine learning models also learning subjective bias. [...]the highly heterogeneous presentation of PD cannot be fully captured by monitoring only a small subset of symptoms thus resulting in too blunt an instrument failing to capture critical symptoms and hence being unable to define a deep phenotype at the individual level. [...]it reduces the costs of bespoke software development in DHT-based exploratory research and clinical studies, especially those related to software implementation and verification. * It enhances confidence in the computational outcomes produced in studies, due to the fact that the software is tested by a large user community. * It provides concise, domain-specific programming abstractions specifically targeting Parkinson’s, thus eliminating the need to write repetitive code. * Contrary to proprietary software, it provides the ability to inspect the algorithms employed and their implementation, thus facilitating the in-depth exploration of the results generated and of any clinical inferences made. The motivation for adopting this approach is our desire to balance the need for a low barrier of entry for developers so as to encourage the adoption of this toolkit; and at the same time, to cater to modern scalable information processing architectures, which are critical in deploying digital assessments at population scale. [...]the combination of an information pipeline approach and an adaptable programming model enables PDkit to effectively support both active and passive monitoring within an integrated framework. |
| Audience | Academic |
| Author | Stamate, Cosmin Saez Pons, Joan Weston, David Roussos, George |
| AuthorAffiliation | Department of Computer Science and Information Systems, Birkbeck College, University of London, London, United Kingdom Hebrew University of Jerusalem, ISRAEL |
| AuthorAffiliation_xml | – name: Department of Computer Science and Information Systems, Birkbeck College, University of London, London, United Kingdom – name: Hebrew University of Jerusalem, ISRAEL |
| Author_xml | – sequence: 1 givenname: Cosmin orcidid: 0000-0003-0386-7331 surname: Stamate fullname: Stamate, Cosmin – sequence: 2 givenname: Joan orcidid: 0000-0003-1812-3722 surname: Saez Pons fullname: Saez Pons, Joan – sequence: 3 givenname: David orcidid: 0000-0001-9459-3430 surname: Weston fullname: Weston, David – sequence: 4 givenname: George orcidid: 0000-0002-7665-7303 surname: Roussos fullname: Roussos, George |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33711008$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_1007_s00702_023_02659_w crossref_primary_10_1038_s43856_024_00481_3 crossref_primary_10_3899_jrheum_2024_0074 |
| Cites_doi | 10.1001/jamaneurol.2018.0809 10.1212/WNL.0000000000006366 10.1147/JRD.2017.2768739 10.1136/jnnp.2007.131045 10.1111/j.1468-1331.2009.02697.x 10.1109/PERCOM.2017.7917848 10.1016/j.biocel.2004.09.009 10.1046/j.1471-4159.1995.64041645.x 10.1145/3136755.3136817 10.1002/mds.22340 10.1016/j.jalz.2005.06.003 10.1007/978-3-642-13119-6_2 10.1016/j.pmcj.2017.12.005 10.1016/j.neucom.2018.03.067 10.1145/857076.857078 |
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| Copyright | COPYRIGHT 2021 Public Library of Science 2021 Stamate et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2021 Stamate et al 2021 Stamate et al |
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| DOI | 10.1371/journal.pcbi.1008833 |
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| Snippet | PDkit is an open source software toolkit supporting the collaborative development of novel methods of digital assessment for Parkinson’s Disease, using symptom... PDkit is an open source software toolkit supporting the collaborative development of novel methods of digital assessment for Parkinson's Disease, using symptom... Since there is currently no cure, clinical care pathways for PD are focused on symptom management, a life-long process that typically includes pharmacological... |
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| SubjectTerms | 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 |
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| Title | PDKit: A data science toolkit for the digital assessment of Parkinson’s Disease |
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