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Machine learning to predict impulse control disorders in Parkinson's disease

Johann Faouzi 1 
1 ARAMIS - Algorithms, models and methods for images and signals of the human brain
SU - Sorbonne Université, Inria de Paris, ICM - Institut du Cerveau = Paris Brain Institute
Abstract : Impulse control disorders are a class of psychiatric disorders characterized by impulsivity. These disorders are common during the course of Parkinson's disease, decrease the quality of life of subjects, and increase caregiver burden. Being able to predict which individuals are at higher risk of developing these disorders and when is of high importance. The objective of this thesis is to study impulse control disorders in Parkinson's disease from the statistical and machine learning points of view, and can be divided into two parts. The first part consists in investigating the predictive performance of the altogether factors associated with these disorders in the literature. The second part consists in studying the association and the usefulness of other factors, in particular genetic data, to improve the predictive performance.
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Submitted on : Friday, March 26, 2021 - 1:25:09 PM
Last modification on : Friday, September 2, 2022 - 12:36:11 PM


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  • HAL Id : tel-03090079, version 2


Johann Faouzi. Machine learning to predict impulse control disorders in Parkinson's disease. Artificial Intelligence [cs.AI]. Sorbonne Université, 2020. English. ⟨NNT : 2020SORUS048⟩. ⟨tel-03090079v2⟩



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