Expert System for Neurocognitive Rehabilitation Based on the Transfer of the ACE-R to CHC Model Factors

Martin Kotyrba (), Hashim Habiballa, Eva Volná, Robert Jarušek, Pavel Smolka, Martin Prášek, Marek Malina, Vladěna Jaremová, Jan Vantuch, Michal Bar and Petr Kulišťák
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Martin Kotyrba: Department of Informatics and Computers, Faculty of Science, University of Ostrava, 30. Dubna 22, 70103 Ostrava, Czech Republic
Hashim Habiballa: Department of Informatics and Computers, Faculty of Science, University of Ostrava, 30. Dubna 22, 70103 Ostrava, Czech Republic
Eva Volná: Department of Informatics and Computers, Faculty of Science, University of Ostrava, 30. Dubna 22, 70103 Ostrava, Czech Republic
Robert Jarušek: Department of Informatics and Computers, Faculty of Science, University of Ostrava, 30. Dubna 22, 70103 Ostrava, Czech Republic
Pavel Smolka: Department of Informatics and Computers, Faculty of Science, University of Ostrava, 30. Dubna 22, 70103 Ostrava, Czech Republic
Martin Prášek: Department of Informatics and Computers, Faculty of Science, University of Ostrava, 30. Dubna 22, 70103 Ostrava, Czech Republic
Marek Malina: Department of Informatics and Computers, Faculty of Science, University of Ostrava, 30. Dubna 22, 70103 Ostrava, Czech Republic
Vladěna Jaremová: University Hospital of Ostrava, 17. Listopadu 1790/5, 70852 Ostrava, Czech Republic
Jan Vantuch: University Hospital of Ostrava, 17. Listopadu 1790/5, 70852 Ostrava, Czech Republic
Michal Bar: University Hospital of Ostrava, 17. Listopadu 1790/5, 70852 Ostrava, Czech Republic
Petr Kulišťák: Faculty of Arts, Charles University, Celetná 20, 11642 Praha, Czech Republic

Mathematics, 2022, vol. 11, issue 1, 1-19

Abstract: This article focuses on developing an expert system applicable to the area of neurocognitive rehabilitation. The benefit of this interdisciplinary research is to propose an expert system that has been adapted based on real patients’ results from the Addenbrooke’s cognitive examination (ACE-R). One of this research’s main results is a unique proposal to transfer the ACE-R result to the CHC (Cattell–Horn–Carroll) intelligence model. This unique approach enables transforming the CHC model domains according to the modified ACE-R factor analysis, which has never been used before. The expert system inference results allow the automated optimized design of a neurorehabilitation plan to train patients’ cognitive functions according to the CHC model. A set of tasks in 6 difficulty levels (Level 1–Level 6) was proposed for each of the nine CHC model domains. For each patient, the ACE-R results helped determine specific CHC domains to be rehabilitated as well as the starting game level for the rehabilitation within each domain. The proposed expert system has been verified on real data of 705 patients and achieved an average error of 5.94% for all CHC model domains. The proposed system is to be included in the outcomes of the research project of the Technology Agency of the Czech Republic as a verified procedure for healthcare providers.

Keywords: expert system; fuzzy logic; ACE-R; CHC; cognitive neurorehabilitation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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