Proposal of neural network model for neurocognitive rehabilitation and its comparison with fuzzy expert system model

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00843989%3A_____%2F23%3AE0110454" target="_blank" >RIV/00843989:_____/23:E0110454 - isvavai.cz</a>

Alternative codes found

RIV/61988987:17310/23:A2402LZK

Result on the web

<a href="https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-023-02321-1" target="_blank" >https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-023-02321-1</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1186/s12911-023-02321-1" target="_blank" >10.1186/s12911-023-02321-1</a>

Result language

angličtina

Original language name

Proposal of neural network model for neurocognitive rehabilitation and its comparison with fuzzy expert system model

Original language description

This article focuses on the development of algorithms for a smart neurorehabilitation system, whose core is made up of artificial neural networks. The authors of the article have proposed a completely unique transfer of ACE-R results to the CHC model. This unique approach allows for the saturation of the CHC model domains according to modified ACE-R factor analysis. The outputs of the proposed algorithm thus enable the automatic creation of a personalized and optimized neurorehabilitation plan for individual patients to train their cognitive functions. A set of tasks in 6 levels of difficulty (level 1 to level 6) was designed for each of the nine CHC model domains. For each patient, the results of the ACE-R screening helped deter-mine the specific CHC domains to be rehabilitated, as well as the initial gaming level for rehabilitation in each domain. The proposed artificial neural network algorithm was adapted to real data from 703 patients. Experimental outputs were compared to the outputs of the initially designed fuzzy expert system, which was trained on the same real data, and all outputs from both systems were statistically evaluated against expert conclusions that were available. It is evident from the conducted experimental study that the smart neurorehabilitation system using artificial neural networks achieved significantly better results than the neurorehabilitation system whose core is a fuzzy expert system. Both algorithms are implemented into a comprehensive neurorehabilitation portal (Eddie), which was supported by a research project from the Technology Agency of the Czech Republic.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10200 - Computer and information sciences

Project

<a href="/en/project/TL02000313" target="_blank" >TL02000313: Intelligent neuro-rehabilitation system for patients with acquired brain damage in early stages of treatment</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

BMC medical informatics and decision making

ISSN

1472-6947

e-ISSN

1472-6947

Volume of the periodical

23

Issue of the periodical within the volume

article 221

Country of publishing house

GB - UNITED KINGDOM

Number of pages

14

Pages from-to

1-14

UT code for WoS article

001086710400003

EID of the result in the Scopus database

2-s2.0-85174299938