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Multimodal Learning and Intelligent Prediction of Symptom Development in Individual Parkinson’s Patients

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...

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Autores principales: Przybyszewski, Andrzej W., Kon, Mark, Szlufik, Stanislaw, Szymanski, Artur, Habela, Piotr, Koziorowski, Dariusz M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5038771/
https://www.ncbi.nlm.nih.gov/pubmed/27649187
http://dx.doi.org/10.3390/s16091498
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author Przybyszewski, Andrzej W.
Kon, Mark
Szlufik, Stanislaw
Szymanski, Artur
Habela, Piotr
Koziorowski, Dariusz M.
author_facet Przybyszewski, Andrzej W.
Kon, Mark
Szlufik, Stanislaw
Szymanski, Artur
Habela, Piotr
Koziorowski, Dariusz M.
author_sort Przybyszewski, Andrzej W.
collection PubMed
description 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 Naïve 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.
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spelling pubmed-50387712016-09-29 Multimodal Learning and Intelligent Prediction of Symptom Development in Individual Parkinson’s Patients Przybyszewski, Andrzej W. Kon, Mark Szlufik, Stanislaw Szymanski, Artur Habela, Piotr Koziorowski, Dariusz M. Sensors (Basel) Article 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 Naïve 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. MDPI 2016-09-14 /pmc/articles/PMC5038771/ /pubmed/27649187 http://dx.doi.org/10.3390/s16091498 Text en © 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Przybyszewski, Andrzej W.
Kon, Mark
Szlufik, Stanislaw
Szymanski, Artur
Habela, Piotr
Koziorowski, Dariusz M.
Multimodal Learning and Intelligent Prediction of Symptom Development in Individual Parkinson’s Patients
title Multimodal Learning and Intelligent Prediction of Symptom Development in Individual Parkinson’s Patients
title_full Multimodal Learning and Intelligent Prediction of Symptom Development in Individual Parkinson’s Patients
title_fullStr Multimodal Learning and Intelligent Prediction of Symptom Development in Individual Parkinson’s Patients
title_full_unstemmed Multimodal Learning and Intelligent Prediction of Symptom Development in Individual Parkinson’s Patients
title_short Multimodal Learning and Intelligent Prediction of Symptom Development in Individual Parkinson’s Patients
title_sort multimodal learning and intelligent prediction of symptom development in individual parkinson’s patients
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5038771/
https://www.ncbi.nlm.nih.gov/pubmed/27649187
http://dx.doi.org/10.3390/s16091498
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