Carmeli Boaz, Casali Paolo, Goldbraich Anna, Goldsteen Abigail, Kent Carmel, Licitra Lisa, Locatelli Paolo, Restifo Nicola, Rinott Ruty, Sini Elena, Torresani Michele, Waks Zeev
IBM Research, Haifa.
Stud Health Technol Inform. 2012;180:604-8.
The personalized medicine era stresses a growing need to combine evidence-based medicine with case based reasoning in order to improve the care process. To address this need we suggest a framework to generate multi-tiered statistical structures we call Evicases. Evicase integrates established medical evidence together with patient cases from the bedside. It then uses machine learning algorithms to produce statistical results and aggregators, weighted predictions, and appropriate recommendations. Designed as a stand-alone structure, Evicase can be used for a range of decision support applications including guideline adherence monitoring and personalized prognostic predictions.
个性化医疗时代强调越来越需要将循证医学与基于案例的推理相结合,以改善医疗过程。为满足这一需求,我们提出了一个框架来生成我们称为“循证案例”(Evicases)的多层统计结构。循证案例将已有的医学证据与床边的患者案例整合在一起。然后,它使用机器学习算法来产生统计结果以及汇总数据、加权预测和适当的建议。作为一个独立的结构设计,循证案例可用于一系列决策支持应用,包括指南依从性监测和个性化预后预测。