## 4 Discussion  
This study appears to be the first to use explainable artificial intelligence (XAI) methods for the implementation of a clinical decision support system (CDSS) for the assessment of nutrition-related geriatric syndromes. As discussed in the Introduction Section, currently, geriatricians face difficulties in diagnosing the following nutrition-related disorders: malnutrition, oropharyngeal dysphagia, dehydration, and eating disorders in dementia. Usually, diagnosis is made using comprehensive geriatric assessment methods and laboratory examinations, which can include (but not necessarily) various questionnaires (e.g., oropharyngeal dysphagia). Even when used, questionnaires are based on inaccurate and subjective estimates of symptoms (usually a 1-4 point scale). Therefore, we have made the assumption that explainable artificial intelligence and fuzzy logic-based reasoning would be the most suitable methods to deal with this problem and to evaluate the relations between different syndromes, which cannot be seen directly from separate questionnaires. The proposed CDSS is based on a decision support model, which is presented in our previous work [6]. The model is based on the knowledge of expert geriatricians and is capable of assessing inaccurate input data and the degree of interrelation between symptoms and syndromes. The results of this study confirm that the previously proposed approach is suitable for practical application in CDSS. As a proof of concept, the CDSS prototype was created and tested with a large number of geriatric patients at the Lithuanian University of Health Sciences Kaunas Hospital. The comparison of the diagnoses provided by the CDSS and expert geriatricians shows that the accuracy of the CDSS is quite high: 87.95% in the case of malnutrition, 87.95% in the case of oropharyngeal dysphagia, 90.36% in the case of malnutrition eating disorders in dementia, 86.75% in the case of dehydration). Although the accuracy of the CDSS diagnoses does not completely match that of the expert geriatrician, the results are quite promising because the CDSS is more accurate than resident geriatricians in diagnosing malnutrition and eating disorders in dementia, and only a few percent less accurate in diagnosing oropharyngeal dysphagia and dehydration. This confirms our next hypothesis that the proposed CDSS, while not a complete replacement for the expert physician, would be perfectly suited to help make a decision for less experienced physicians. The CDSS can be used as an additional tool useful not only for geriatricians but also for general practitioners. Since the qualification of general practitioners is in line with the experience of junior resident geriatricians, the developed CDSS will diagnose geriatric syndromes much more accurately and can be recommended as a support tool to help general practitioners (or other non-geriatricians) identify geriatric disorders. One of the key benefits of XAI-based CDSS is that physicians can easily verify why and on what basis the CDSS provided a specific assessment of the syndrome and use it to determine the reasons for the discrepancy in the assessment. In this way, less experienced physicians can understand where their assessment errors are, and expert physicians can identify areas for improvement in the system. Despite the promising results of this study, there are several ways to increase the precision of the proposed CDSS. One direction for further research is applying a more sophisticated algorithm to convert the CDSS risk-certainty pairs into concrete diagnoses. Another direction can include improving questionnaires and including laboratory test results in the decision-making process. Research into solving this problem is already underway. In general, there are many ways to improve the proposed nutrition-related geriatric syndromes assessment model and the CDSS.