Explainable Artificial Intelligence-Based Decision Support System for Assessing the Nutrition-Related Geriatric Syndromes

• Published in: Applied sciences 2021-12, Vol.11 (24), p.11763
• Main Authors: Petrauskas, Vytautas, Jasinevicius, Raimundas, Damuleviciene, Gyte, Liutkevicius, Agnius, Janaviciute, Audrone, Lesauskaite, Vita, Knasiene, Jurgita, Meskauskas, Zygimantas, Dovydaitis, Juozas, Kazanavicius, Vygintas, Bitinaite-Paskeviciene, Raminta
• Format: Article
• Language: English
• Subjects: Accuracy; Alzheimer's disease; Artificial intelligence; Cancer; clinical decision support system (CDSS); Cognitive models; computing with words (CWW); Decision making; Decision support systems; Dehydration; Dementia; Dementia disorders; Dysphagia; Eating disorders; explainable artificial intelligence (XAI); Family physicians; Fuzzy logic; fuzzy logic-based reasoning; geriatric syndrome; Geriatrics; Health risks; Hospitalization; Hospitals; Malnutrition; Medical personnel; Medical research; Medical screening; Nutrition; Nutritional status; Older people; Patients; Performance evaluation; Physicians; Questionnaires; Risk analysis; Risk factors; Signs and symptoms; Software
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app112411763

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.