Department of Informatics and Computers , University of Ostrava, Faculty of Science, 30.dubna 22, Ostrava, 70103, Czech Republic.
University Hospital of Ostrava, 17. listopadu 1790/5, Ostrava, 70852, Czech Republic.
BMC Med Inform Decis Mak. 2023 Oct 16;23(1):221. doi: 10.1186/s12911-023-02321-1.
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.
本文专注于开发一种智能神经康复系统的算法，其核心由人工神经网络组成。本文的作者提出了一种将 ACE-R 结果完全独特地转移到 CHC 模型的方法。这种独特的方法允许根据修改后的 ACE-R 因子分析使 CHC 模型域饱和。因此，所提出算法的输出能够为每个患者自动创建个性化和优化的神经康复计划，以训练他们的认知功能。为 CHC 模型的九个领域中的每一个领域设计了一套六个难度级别（级别 1 到级别 6）的任务。对于每个患者，ACE-R 筛选的结果有助于确定要康复的特定 CHC 领域，以及每个领域康复的初始游戏级别。所提出的人工神经网络算法适用于来自 703 名患者的真实数据。实验输出与最初设计的基于模糊专家系统的输出进行了比较，该系统是基于相同的真实数据进行训练的，并且对两个系统的所有输出都进行了统计学评估，以与可用的专家结论进行比较。从进行的实验研究中可以明显看出，使用人工神经网络的智能神经康复系统比以模糊专家系统为核心的神经康复系统取得了显著更好的结果。这两种算法都被集成到一个全面的神经康复门户（Eddie）中，该门户得到了捷克技术局研究项目的支持。
