User-Centered Development of a Clinical Decision Support System

Scientific progress is offering increasingly better ways to tailor a patient’s treatment to the patient’s needs, i.e., better support for optimal clinical decision-making can be offered. Choosing the appropriate treatment for a patient depends on numerous factors, including pathology results, tumor stage, genetic, and molecular characteristics. Bayesian networks are a type of probabilistic artificial intelligence, which in principle would be suitable to support complex clinical decision-making. However, most clinicians do not have experience with these networks. This paper describes an approach of developing a clinical decision support system based on Bayesian networks, that does not require insight knowledge about the underlying computational model for its use. It is developed as a therapy-oriented approach with a focus on usability and explainability. The approach features the computation and presentation of individualized treatment recommendations, comparison of treatments and patient cases, as well as explanations and visualizations providing additional information on the current patient case.