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

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.

Automated summary

This article focuses on the development of an expert system for neurocognitive rehabilitation. The unique approach of this interdisciplinary research is to adapt the expert system based on real patients' results from the ACE-R. The expert system allows for an automated optimized design of a neurorehabilitation plan using the CHC model, achieving an average error of 5.94% for all domains.