Explainable AI-based clinical decision support system for hearing disorders

In clinical system design, human-computer interaction and explainability are important topics of research. Clinical systems need to provide users with not only results but also an account of their behaviors. In this research, we propose a knowledge-based clinical decision support system (CDSS) for the diagnosis and therapy of hearing disorders, such as tinnitus, hyperacusis, and misophonia. Our prototype eTRT system offers an explainable output that we expect to increase its trustworthiness and acceptance in the clinical setting. Within this paper, we: (1) present the problem area of tinnitus and its treatment; (2) describe our data-driven approach based on machine learning, such as association- and action rule discovery; (3) present the evaluation results from the inference on the extracted rule-based knowledge and chosen test cases of patients; (4) discuss advantages of explainable output incorporated into a graphical user interface; (5) conclude with the results achieved and directions for future work.