A pharmaceutical therapy recommender system enabling shared decision-making

Data-based clinical decision support systems (CDSSs) can provide personalized support in medical applications. Such systems are expected to play an increasingly important role in the future of healthcare. Within this work, we demonstrate an exemplary CDSS which provides individualized pharmaceutical drug recommendations to physicians and patients. The core of the proposed system is a neighborhood-based collaborative filter (CF) that yields data-based recommendations. CFs are capable of integrating data at different scale levels and a multivariate outcome measure. This publication provides a detailed literature review, a holistic comparison of various implementations of CF algorithms, and a prototypical graphical user interface (GUI). We show that similarity measures, which automatically adapt to attribute weights and data distribution perform best. The illustrated user-friendly prototype is intended to graphically facilitate explainable recommendations and provide additional evidence-based information tailored to a target patient. The proposed solution or elements of it, respectively, may serve as a template for future CDSSs that support physicians to identify the most appropriate therapy and enable a shared decision-making process between physicians and patients.