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

Kostek B, Kaszuba K, Zwan P, Robowski P, Slawek J - Diagn Pathol (2012)

Bottom Line: 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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Multimedia Systems Department, Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Gdansk, Poland. bozenka@sound.eti.pg.gda.pl

ABSTRACT

Unlabelled: 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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An example of a decision table filled in by the clinicians.
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Figure 2: An example of a decision table filled in by the clinicians.

Mentions: Five experts (neurologists) participated in the creation of the decision tables, but only four of them evaluated the current patients' data. Their task was to assign a criterion--a decision attribute ("stable", "worsening", "alert") to every possible pair of the given UPDRS item. Changes from the higher to the lower score were not evaluated. For each record in the decision table a histogram of experts' decisions was created. Examples of such decision tables and a histogram are presented respectively in Figures 2 and 3. In the histograms information about number of experts voting for each criterion is presented (see Figure 3).


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

Kostek B, Kaszuba K, Zwan P, Robowski P, Slawek J - Diagn Pathol (2012)

An example of a decision table filled in by the clinicians.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3313854&req=5

Figure 2: An example of a decision table filled in by the clinicians.
Mentions: Five experts (neurologists) participated in the creation of the decision tables, but only four of them evaluated the current patients' data. Their task was to assign a criterion--a decision attribute ("stable", "worsening", "alert") to every possible pair of the given UPDRS item. Changes from the higher to the lower score were not evaluated. For each record in the decision table a histogram of experts' decisions was created. Examples of such decision tables and a histogram are presented respectively in Figures 2 and 3. In the histograms information about number of experts voting for each criterion is presented (see Figure 3).

Bottom Line: 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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Multimedia Systems Department, Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Gdansk, Poland. bozenka@sound.eti.pg.gda.pl

ABSTRACT

Unlabelled: 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.

Show MeSH
Related in: MedlinePlus