Multimodal Learning and Intelligent Prediction of Symptom Development in Individual Parkinson's Patients
Authors
Przybyszewski, Andrzej W.Kon, Mark
Szlufik, Stanislaw
Szymanski, Artur
Habela, Piotr
Koziorowski, Dariusz M.
UMass Chan Affiliations
Department of NeurologyDocument Type
Journal ArticlePublication Date
2016-09-14Keywords
decision rulesmachine learning
neurodegenerative disease
rough set
Clinical Epidemiology
Nervous System Diseases
Neurology
Statistics and Probability
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We still do not know how the brain and its computations are affected by nerve cell deaths and their compensatory learning processes, as these develop in neurodegenerative diseases (ND). Compensatory learning processes are ND symptoms usually observed at a point when the disease has already affected large parts of the brain. We can register symptoms of ND such as motor and/or mental disorders (dementias) and even provide symptomatic relief, though the structural effects of these are in most cases not yet understood. It is very important to obtain early diagnosis, which can provide several years in which we can monitor and partly compensate for the disease's symptoms, with the help of various therapies. In the case of Parkinson's disease (PD), in addition to classical neurological tests, measurements of eye movements are diagnostic. We have performed measurements of latency, amplitude, and duration in reflexive saccades (RS) of PD patients. We have compared the results of our measurement-based diagnoses with standard neurological ones. The purpose of our work was to classify how condition attributes predict the neurologist's diagnosis. For n = 10 patients, the patient age and parameters based on RS gave a global accuracy in predictions of neurological symptoms in individual patients of about 80%. Further, by adding three attributes partly related to patient 'well-being' scores, our prediction accuracies increased to 90%. Our predictive algorithms use rough set theory, which we have compared with other classifiers such as Naive Bayes, Decision Trees/Tables, and Random Forests (implemented in KNIME/WEKA). We have demonstrated that RS are powerful biomarkers for assessment of symptom progression in PD.Source
Sensors (Basel). 2016 Sep 14;16(9). pii: E1498. doi: 10.3390/s16091498. Link to article on publisher's siteDOI
10.3390/s16091498Permanent Link to this Item
http://hdl.handle.net/20.500.14038/40152PubMed ID
27649187Related Resources
Link to Article in PubMedRights
© 2016 by the authors; licensee MDPI, Basel, Switzerland.Distribution License
http://creativecommons.org/licenses/by/4.0/ae974a485f413a2113503eed53cd6c53
10.3390/s16091498
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Except where otherwise noted, this item's license is described as © 2016 by the authors; licensee MDPI, Basel, Switzerland.