Institut für Biomedizinische Technik, Technische Universität Dresden, Dresden, Germany.
Zentrum für Evidenzbasierte Gesundheitsversorgung, Universitätsklinikum Dresden, Dresden, Germany.
J Healthc Eng. 2017;2017:8659460. doi: 10.1155/2017/8659460. Epub 2017 Mar 28.
We present a system for data-driven therapy decision support based on techniques from the field of recommender systems. Two methods for therapy recommendation, namely, and , are proposed. Both algorithms aim to predict the individual response to different therapy options using diverse patient data and recommend the therapy which is assumed to provide the best outcome for a specific patient and time, that is, consultation. The proposed methods are evaluated using a clinical database incorporating patients suffering from the autoimmune skin disease psoriasis. The proves to generate both better outcome predictions and recommendation quality. However, due to sparsity in the data, this approach cannot provide recommendations for the entire database. In contrast, the performs worse on average but covers more consultations. Consequently, both methods profit from a combination into an overall recommender system.
我们提出了一种基于推荐系统领域技术的数据驱动治疗决策支持系统。提出了两种治疗推荐方法，即 和 。这两种算法都旨在使用不同的患者数据来预测个体对不同治疗方案的反应，并推荐被认为对特定患者和时间（即咨询）提供最佳结果的治疗方案。所提出的方法使用包含患有自身免疫性皮肤病银屑病的患者的临床数据库进行评估。结果表明， 不仅可以生成更好的结果预测，而且还可以提高推荐质量。但是，由于数据稀疏，该方法无法为整个数据库提供建议。相比之下， 平均性能较差，但涵盖了更多的咨询。因此，这两种方法都受益于结合成一个整体的推荐系统。
