Supporting the Incorporation of Individual Patient Preferences for Decision Support in Breast Cancer Therapy

With the rise of personalized medicine, the number of individualized treatment options and related decisions is increasing tremendously. Thereby, the acquisition, incorporation and representation of the patient’s individual preferences in upcoming, modern, AI-based medical decision support systems play a decisive role. E.g., for patients with advanced breast cancer, there are various therapeutic options associated with different outcomes to choose from. In our contribution we show a first approach to model Preference Elicitation (PE) via card sorting using a utility function. Based on this, we present further ideas for extending and improving the approach.