Seroussi Brigitte, Lamy Jean-Baptiste, Muro Naiara, Larburu Nekane, Sekar Booma Devi, Guézennec Gilles, Bouaud Jacques
Sorbonne Université, Université Paris 13, Sorbonne Paris Cité, INSERM, UMR S_1142, LIMICS, Paris, France.
eHeatlh and Biomedical Applications, Vicomtech-IK4, Donostia-San Sebastian, Spain.
Stud Health Technol Inform. 2018;255:190-194.
DESIREE is a European-funded project to improve the management of primary breast cancer. We have developed three decision support systems (DSSs), a guideline-based, an experience-based, and a case-based DSSs, resp. GL-DSS, EXP-DSS, and CB-DSS, that operate simultaneously to offer an enriched multi-modal decision support to clinicians. A breast cancer knowledge model has been built to describe within a common ontology the data model and the termino-ontological knowledge used for representing breast cancer patient cases. It allows for rule-based and subsumption-based reasoning in the GL-DSS to provide best patient-centered reconciled care plans. It also allows for using semantic similarity in the retrieval algorithm implemented in the CB-DSS. Rainbow boxes are used to display patient cases similar to a given query patient. This innovative visualization technique translates the question of deciding the most appropriate treatment into a question of deciding the colour dominance among boxes.
DESIREE是一个由欧洲资助的旨在改善原发性乳腺癌管理的项目。我们开发了三个决策支持系统（DSS），分别是基于指南的、基于经验的和基于案例的DSS，即GL-DSS、EXP-DSS和CB-DSS，它们同时运行，为临床医生提供丰富的多模式决策支持。已经构建了一个乳腺癌知识模型，用于在一个通用本体中描述用于表示乳腺癌患者病例的数据模型和术语本体知识。它允许在GL-DSS中进行基于规则和基于包含的推理，以提供最佳的以患者为中心的协调护理计划。它还允许在CB-DSS中实现的检索算法中使用语义相似性。彩虹框用于显示与给定查询患者相似的患者病例。这种创新的可视化技术将决定最合适治疗方法的问题转化为决定框之间颜色优势的问题。
