Abstract
In this paper, we propose a multi-agent framework to deal with situations involving uncertain or inconsistent information located in a distributed environment which cannot be combined into a single knowledge base. To this end, we introduce an inquiry dialogue approach based on a combination of possibilistic logic and a formal argumentation-based theory, where possibilistic logic is used to capture uncertain information, and the argumentation-based approach is used to deal with inconsistent knowledge in a distributed environment. We also modify the framework of earlier work, so that the system is not only easier to implement but also more suitable for educational purposes. The suggested approach is implemented in a clinical decision-support system in the domain of dementia diagnosis. The approach allows the physician to suggest a hypothetical diagnosis in a patient case, which is verified through the dialogue if sufficient patient information is present. If not, the user is informed about the missing information and potential inconsistencies in the information as a way to provide support for continuing medical education. The approach is presented, discussed, and applied to one scenario. The results contribute to the theory and application of inquiry dialogues in situations where the data are uncertain and inconsistent.
Highlights
Clinical decision-support systems (CDSSs) use Artificial Intelligence (AI) to help doctors reach diagnostic decisions and are playing an increasingly important role in clinical practice [29]
In domains such as the medical domain, knowledge bases sometimes cannot be combined due to various constrictions, and the knowledge applied in reasoning and decision making is often uncertain and inconsistent
Possibilistic logic is used for capturing uncertain information, and an argumentation framework is used for dealing with inconsistent knowledge in reasoning about a diagnosis
Summary
Clinical decision-support systems (CDSSs) use Artificial Intelligence (AI) to help doctors reach diagnostic decisions and are playing an increasingly important role in clinical practice [29]. Reasoning with possibilities gives a different starting point, where if knowing nothing at all, all potential diagnoses are possible, which may help the novice clinician to not jump to conclusions too soon [35] This approach follows the terminology applied in some diagnostic criteria where the medical community has tried to translate the uncertainty in EBM-based statistical information into formulations useful in clinical practice using terms like possible, probable, and unlikely (e.g., [28]). Using the methods we develop based on possibilistic logic and argumentation theory, traditionally difficult situations where the data are uncertain and inconsistent can be properly dealt with transparently This approach provides transparency so the clinician can follow the reasoning and decision-making process and make more well-founded medical decisions, and using the system will provide continued medical education during everyday clinical practice. The paper ends with conclusions and an outline of future work
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