Mohammed Osama, Benlamri Rachid
Department of Software Engineering, Lakehead University, 955 Oliver Road, Thunder Bay, P7B 5E1, ON, Canada,
J Med Syst. 2014 Oct;38(10):79. doi: 10.1007/s10916-014-0079-0. Epub 2014 Sep 2.
In this paper we describe a novel model for differential diagnosis designed to make recommendations by utilizing semantic web technologies. The model is a response to a number of requirements, ranging from incorporating essential clinical diagnostic semantics to the integration of data mining for the process of identifying candidate diseases that best explain a set of clinical features. We introduce two major components, which we find essential to the construction of an integral differential diagnosis recommendation model: the evidence-based recommender component and the proximity-based recommender component. Both approaches are driven by disease diagnosis ontologies designed specifically to enable the process of generating diagnostic recommendations. These ontologies are the disease symptom ontology and the patient ontology. The evidence-based diagnosis process develops dynamic rules based on standardized clinical pathways. The proximity-based component employs data mining to provide clinicians with diagnosis predictions, as well as generates new diagnosis rules from provided training datasets. This article describes the integration between these two components along with the developed diagnosis ontologies to form a novel medical differential diagnosis recommendation model. This article also provides test cases from the implementation of the overall model, which shows quite promising diagnostic recommendation results.
在本文中，我们描述了一种用于鉴别诊断的新型模型，该模型旨在通过利用语义网技术来做出推荐。该模型是对一系列需求的回应，这些需求从纳入基本的临床诊断语义到整合数据挖掘以识别最能解释一组临床特征的候选疾病的过程。我们引入了两个主要组件，我们发现它们对于构建完整的鉴别诊断推荐模型至关重要：基于证据的推荐组件和基于相似度的推荐组件。这两种方法都由专门设计用于实现诊断推荐过程的疾病诊断本体驱动。这些本体是疾病症状本体和患者本体。基于证据的诊断过程基于标准化临床路径制定动态规则。基于相似度的组件利用数据挖掘为临床医生提供诊断预测，并从提供的训练数据集中生成新的诊断规则。本文描述了这两个组件之间的整合以及所开发的诊断本体，以形成一种新型的医学鉴别诊断推荐模型。本文还提供了整个模型实施的测试用例，显示出颇具前景的诊断推荐结果。
