Automatic assessment of the motor state of the Parkinson's disease patient--a case study

This paper presents a novel methodology in which the Unified Parkinson's Disease Rating Scale (UPDRS) data processed with a rule-based decision algorithm is used to predict the state of the Parkinson's Disease patients. The research was carried out to investigate whether the advancement of...

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Autores principales: Kostek, Bozena, Kaszuba, Katarzyna, Zwan, Pawel, Robowski, Piotr, Slawek, Jaroslaw
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2012
Materias:
Methodology
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3313854/
https://www.ncbi.nlm.nih.gov/pubmed/22340508
http://dx.doi.org/10.1186/1746-1596-7-18
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author Kostek, Bozena
Kaszuba, Katarzyna
Zwan, Pawel
Robowski, Piotr
Slawek, Jaroslaw
author_facet Kostek, Bozena
Kaszuba, Katarzyna
Zwan, Pawel
Robowski, Piotr
Slawek, Jaroslaw
author_sort Kostek, Bozena
collection PubMed
description This paper presents a novel methodology in which the Unified Parkinson's Disease Rating Scale (UPDRS) data processed with a rule-based decision algorithm is used to predict the state of the Parkinson's Disease patients. The research was carried out to investigate whether the advancement of the Parkinson's Disease can be automatically assessed. For this purpose, past and current UPDRS data from 47 subjects were examined. The results show that, among other classifiers, the rough set-based decision algorithm turned out to be most suitable for such automatic assessment. VIRTUAL SLIDES: The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/1563339375633634.
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spelling pubmed-33138542012-04-04 Automatic assessment of the motor state of the Parkinson's disease patient--a case study Kostek, Bozena Kaszuba, Katarzyna Zwan, Pawel Robowski, Piotr Slawek, Jaroslaw Diagn Pathol Methodology This paper presents a novel methodology in which the Unified Parkinson's Disease Rating Scale (UPDRS) data processed with a rule-based decision algorithm is used to predict the state of the Parkinson's Disease patients. The research was carried out to investigate whether the advancement of the Parkinson's Disease can be automatically assessed. For this purpose, past and current UPDRS data from 47 subjects were examined. The results show that, among other classifiers, the rough set-based decision algorithm turned out to be most suitable for such automatic assessment. VIRTUAL SLIDES: The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/1563339375633634. BioMed Central 2012-02-19 /pmc/articles/PMC3313854/ /pubmed/22340508 http://dx.doi.org/10.1186/1746-1596-7-18 Text en Copyright ©2012 Kostek et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Methodology
Kostek, Bozena
Kaszuba, Katarzyna
Zwan, Pawel
Robowski, Piotr
Slawek, Jaroslaw
Automatic assessment of the motor state of the Parkinson's disease patient--a case study
title Automatic assessment of the motor state of the Parkinson's disease patient--a case study
title_full Automatic assessment of the motor state of the Parkinson's disease patient--a case study
title_fullStr Automatic assessment of the motor state of the Parkinson's disease patient--a case study
title_full_unstemmed Automatic assessment of the motor state of the Parkinson's disease patient--a case study
title_short Automatic assessment of the motor state of the Parkinson's disease patient--a case study
title_sort automatic assessment of the motor state of the parkinson's disease patient--a case study
topic Methodology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3313854/
https://www.ncbi.nlm.nih.gov/pubmed/22340508
http://dx.doi.org/10.1186/1746-1596-7-18
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