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

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/61988987:17310/23:A2402LZK

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

Projekt

<a href="/cs/project/TL02000313" target="_blank" >TL02000313: Chytrý neuro-rehabilitační systém pro pacienty se získaným poškozením mozku v časných stádiích léčby</a><br>

Návaznosti

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

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název periodika

BMC medical informatics and decision making

ISSN

1472-6947

e-ISSN

1472-6947

Svazek periodika

23

Číslo periodika v rámci svazku

article 221

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

14

Strana od-do

1-14

Kód UT WoS článku

001086710400003

EID výsledku v databázi Scopus

2-s2.0-85174299938