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.

基于数据的临床决策支持系统 (CDSS) 可以在医疗应用中提供个性化支持。预计此类系统将在未来的医疗保健中发挥越来越重要的作用。在这项工作中，我们展示了一个典型的 CDSS，它为医生和患者提供个性化的药物推荐。所提出系统的核心是一个基于邻域的协同过滤器 (CF)，它产生基于数据的推荐。CF 能够整合不同规模级别的数据和多变量结果测量。该出版物提供了详细的文献综述、CF 算法各种实现的整体比较，以及典型的图形用户界面 (GUI)。我们展示了自动适应属性权重和数据分布的相似性度量表现最好。图示的用户友好原型旨在以图形方式促进可解释的建议，并提供针对目标患者量身定制的额外循证信息。所提议的解决方案或其中的元素可分别作为未来 CDSS 的模板，支持医生确定最合适的治疗方法，并在医生和患者之间实现共享决策过程。