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The use of artificial intelligence in geriatrics is very promising and relevant, as the diagnosis of a geriatric patient is a complex, experience-based, and time-consuming process that involves a variety of questionnaires and subjective and inaccurate patient responses. This paper proposes the explainable artificial intelligence-based (XAI) clinical decision support system (CDSS) to assess nutrition-related factors (symptoms) and to determine the likelihood of geriatric patient health risks associated with four syndromes: malnutrition, oropharyngeal dysphagia, dehydration, and eating disorders in dementia. The proposed system's prototype was tested under real conditions at the geriatric department of Lithuanian University of Health Sciences Kaunas Hospital. The subjects of this study were 83 geriatric patients with various health conditions. The assessments of the nutritional status and syndromes of the patients provided by the CDSS were compared with the diagnoses of the physicians obtained using standard assessment methods. The results show that proposed CDSS can efficiently diagnose nutrition-related geriatric syndromes with high accuracy: 87.95% for malnutrition, 87.95% for oropharyngeal dysphagia, 90.36% for eating disorders in dementia, and 86.75% for dehydration. The research confirms that the proposed XAI-based CDSS is an effective tool, able to assess nutrition-related health risk factors and their dependencies and, in some cases, makes even a more accurate decision than a less experienced physician. [ABSTRACT FROM AUTHOR]

A substantial amount of scientific research and publications in the smart home domain deal with different aspects of fuzzy logic application. However, we feel there is a general lack of complex modelling done of the whole smart home environment. So, the main goal of this paper is threefold: 1) to present a virtual model of a smart home environment to be intellectualized, 2) to demonstrate the efficiency of Mamdani type inference procedure in modelling of intellectual behaviour of the environment and 3) to show that it is possible to minimize the quantity of fuzzy rules by taking the hierarchical approach and adding the Takagi-Sugeno inference procedure to empower it. The results of computerized modelling and simulation are delivered in the paper demonstrating the practical viability and efficiency of the theoretical approach. The model of the intelligent home environment was used in two projects. Since a formalized analytical method for evaluating the sensitivity of system parameters still does not exist, the experimental sensitivity simulation in our case is performed and presented in this paper. Characteristics obtained in the modelled virtual environment can be easily expanded and used in a real home environment by changing pixels into real coordinates and the light intensity into other real variables. [ABSTRACT FROM AUTHOR]