The DESIREE project has developed a platform offering several complementary therapeutic decision support modules to improve the quality of care for breast cancer patients. All modules are operating consistently with a common breast cancer knowledge model (BCKM) following the generic entity-attribute-value model. The BCKM is formalized as an ontology including both the data model to represent clinical patient information and the termino-ontological model to represent the application domain concepts. This ontological model is used to describe data semantics and to allow for reasoning at different levels of abstraction. We present the guideline-based decision support module (GL-DSS). Three breast cancer clinical practice guidelines have been formalized as decision rules including evidence levels, conformance levels, and two types of dependency, “refinement” and “complement”, used to build complete care plans from the reconciliation of atomic recommendations. The system has been assessed on 138 decisions previously made without the system and re-played with the system after a washout period on simulated tumor boards (TBs) in three pilot sites. When TB clinicians changed their decision after using the GL-DSS, it was for a better decision than the decision made without the system in 75 % of the cases.

DESIREE 项目开发了一个平台，提供多种互补的治疗决策支持模块，以提高乳腺癌患者的护理质量。所有模块都按照通用的实体-属性-值模型与通用乳腺癌知识模型 (BCKM) 一致运行。BCKM 被形式化为一个本体，包括表示临床患者信息的数据模型和表示应用领域概念的术语本体模型。该本体模型用于描述数据语义并允许在不同抽象级别进行推理。我们提出了基于指南的决策支持模块 (GL-DSS)。三项乳腺癌临床实践指南已正式确定为决策规则，包括证据级别、一致性级别和两种类型的依赖性，“细化”和“补充”，用于根据原子建议的协调来构建完整的护理计划。该系统已经根据之前在没有该系统的情况下做出的 138 项决定进行了评估，并在三个试点站点的模拟肿瘤板 (TB) 上进行了冲洗期后重新使用该系统进行了评估。当结核病临床医生在使用 GL-DSS 后改变他们的决定时，在 75% 的案例中，这是为了比在没有该系统的情况下做出的决定更好的决定。