XAI-based Medical Decision Support System Model

Petrauskas, Vytautas; Damulevičienė, Gytė; Dobrovolskis, Algirdas; Dovydaitis, Juozas; Janavičiūtė, Audronė; Jasinevičius, Raimundas; Kazanavičius, Egidijus; Knašienė, Jurgita; Lesauskaitė, Vita; Liutke, Agnius

doi:10.29322/ijsrp.10.12.2020.p10869

Cited by 3 publications

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“…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.…”

Section: Discussionmentioning

confidence: 99%

“…Such a structure is called FEM, the fuzzy expert map as it was proposed in [27] and mentioned above. It should be stressed that the model described in [6] uses only threats − − → TH e and weaknesses − − → WK e as the inputs to corresponding SWOT elements, where e ∈ {A, B, C, D} (see Figure 2). In this simplified case, each SWOT element summarizes and normalizes the assessments of the symptoms of a particular syndrome as threats ( − − → TH e ) in numerical and/or verbal form, and summarizes and normalizes the responses to the questionnaire for each syndrome as weaknesses ( −−→ WK e ) specific to that syndrome and is also processed numerically and/or verbally.…”

Section: Enhanced Swot Maps and Fuzzy Expert Maps For Risk Evaluationmentioning

confidence: 99%

“…This paper presents the software implementation and evaluation of the clinical decision support system (CDSS) for the geriatric department of Lithuanian University of Health Sciences Kaunas Hospital, which solves the previously mentioned problems related to the geriatric diagnosis process, allowing even less experienced medical personnel to make more accurate decisions faster and easier. The prototype of CDSS is based on the original explainable artificial intelligence (XAI)-based decision support model presented in the previous scientific work [6] published by the authors of this paper. The proposed model was implemented in the CDSS prototype tested under real conditions at the geriatric department of Lithuanian University of Health Sciences Kaunas Hospital.…”

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confidence: 99%

“…This paper describes the application of explainable artificial intelligence (XAI) for the implementation of the CDSS to diagnose geriatric diseases and syndromes related to malnutrition. Studies related to the diagnosis of nutritional disorders and the identification of related syndromes using artificial intelligence and CDSS are not found in the scientific literature, except our initial study [6], which proposes a theoretical XAI-based decision support model for nutrition-related geriatric disorders.…”

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confidence: 99%

“…This paper presents the real-world experimental evaluation of the theoretical XAIbased CDSS model [6] with real patient data. The aim of this study is to develop the software implementation of web-based CDSS and to evaluate the effectiveness of the proposed system by comparing its diagnoses with those of medical experts.…”

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

et al. 2021

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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.

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Section: Enhanced Swot Maps and Fuzzy Expert Maps For Risk Evaluationmentioning

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

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A Rule-Based Approach for Interpretable Intensity-Modulated Radiation Therapy Treatment Selection

Gonzalez-Garcia,

Fumanal-Idocin,

Do Rio

et al. 2024

2024 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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About the New Methodology and XAI-Based Software Toolkit for Risk Assessment

Meskauskas

Kazanavičius

2022

Sustainability

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There are different approaches in different areas of what the risk is. ISO 31000 risk management standards describe risk as the effect of uncertainty on objectives. Many existing risk assessment procedures are based on the assumption that risk is the amount of any damage or loss multiplied by the probability of an event that could cause the damage. We are proposing a new risk approach, based on Hillson’s positive risk philosophy, that risk is not just a threat but also a composition of new opportunities, efforts that need to be put in, and uncertainty. For this approach, we composed a risk formula and a methodology based on that formula. A prototypical software tool was developed, and an experiment was performed using this tool to evaluate the risk of several interconnected projects and validate the developed risk assessment methodology. It should be mentioned that, in the methodology, the decision-making process is performed traceably; therefore, it can be stated that it has explainable artificial intelligence (XAI) traits.

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XAI-based Medical Decision Support System Model

Cited by 3 publications

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

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

A Rule-Based Approach for Interpretable Intensity-Modulated Radiation Therapy Treatment Selection

About the New Methodology and XAI-Based Software Toolkit for Risk Assessment

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