Ramisetty K, Christopher J, Panda S, Lazarus BS, Dayalan J. An Explainable Knowledge-Based System Using Subjective Preferences and Objective Data for Ranking Decision Alternatives.
Methods Inf Med 2022;
61:111-122. [PMID:
36220110 DOI:
10.1055/s-0042-1756650]
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Abstract
BACKGROUND
Allergy is a hypersensitive reaction that occurs when the allergen reacts with the immune system. The prevalence and severity of the allergies are uprising in South Asian countries. Allergy often occurs in combinations which becomes difficult for physicians to diagnose.
OBJECTIVES
This work aims to develop a decision-making model which aids physicians in diagnosing allergy comorbidities. The model intends to not only provide rational decisions, but also explainable knowledge about all alternatives.
METHODS
The allergy data gathered from real-time sources contain a smaller number of samples for comorbidities. Decision-making model applies three sampling strategies, namely, ideal, single, and complete, to balance the data. Bayes theorem-based probabilistic approaches are used to extract knowledge from the balanced data. Preference weights for attributes with respect to alternatives are gathered from a group of domain-experts affiliated to different allergy testing centers. The weights are combined with objective knowledge to assign confidence values to alternatives. The system provides these values along with explanations to aid decision-makers in choosing an optimal decision.
RESULTS
Metrics of explainability and user satisfaction are used to evaluate the effectiveness of the system in real-time diagnosis. Fleiss' Kappa statistic is 0.48, and hence the diagnosis of experts is said to be in moderate agreement. The decision-making model provides a maximum of 10 suitable and relevant pieces of evidence to explain a decision alternative. Clinicians have improved their diagnostic performance by 3% after using CDSS (77.93%) with a decrease in 20% of time taken.
CONCLUSION
The performance of less-experienced clinicians has improved with the support of an explainable decision-making model. The code for the framework with all intermediate results is available at https://github.com/kavya6697/Allergy-PT.git.
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