An Ontology-Based Interpretable Fuzzy Decision Support System for Diabetes Diagnosis
An Ontology-Based Interpretable Fuzzy Decision Support System for Diabetes Diagnosis
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DOI：
10.1109/access.2018.2852004
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发表时间：
2018-01-01
期刊：
影响因子：
3.9
通讯作者：
Kwak, Kyung-Sup
中科院分区：
文献类型：
--
作者：
El-Sappagh, Shaker;Alonso, Jose M.;Kwak, Kyung-Sup
糖尿病是一种严重的慢性疾病。临床决策支持系统（CDSS）诊断糖尿病的重要性，导致了广泛的研究工作，以提高这些系统的准确性，适用性，可解释性和互操作性。然而，这个问题仍然需要优化。基于模糊规则的系统适用于医学领域，其中可解释性是主要关注点。医疗领域是数据密集型的，使用电子健康记录数据来构建FRBS知识库和模糊集是至关重要的。经常需要多个变量来确定正确和个性化的诊断，这通常使得难以做出准确和及时的决定。在本文中，我们提出并实现了一个新的语义可解释的FRBS框架糖尿病诊断。该框架使用多方面的知识模糊推理，本体推理，和模糊层次分析法（FAHP），以提供一个更直观和准确的设计。首先，我们建立了一个两层的层次和可解释的FRBS，然后，我们通过整合基于SNOMED CT标准本体的本体推理过程来改进这一点。我们采用FAHP来确定每个子FRBS的相对医学重要性。建议的系统提供了许多独特的和关键的改进，关于实施一个准确的，动态的，语义智能的，可解释的CDSS。该系统在模糊规则的评价过程中考虑了糖尿病并发症和症状概念的本体语义相似度。使用真实的数据集对该框架进行了测试，结果表明该系统如何帮助医生和患者准确诊断糖尿病。
Diabetes is a serious chronic disease. The importance of clinical decision support systems (CDSSs) to diagnose diabetes has led to extensive research efforts to improve the accuracy, applicability, interpretability, and interoperability of these systems. However, this problem continues to require optimization. Fuzzy rule-based systems are suitable for the medical domain, where interpretability is a main concern. The medical domain is data-intensive, and using electronic health record data to build the FRBS knowledge base and fuzzy sets is critical. Multiple variables are frequently required to determine a correct and personalized diagnosis, which usually makes it difficult to arrive at accurate and timely decisions. In this paper, we propose and implement a new semantically interpretable FRBS framework for diabetes diagnosis. The framework uses multiple aspects of knowledge-fuzzy inference, ontology reasoning, and a fuzzy analytical hierarchy process (FAHP) to provide a more intuitive and accurate design. First, we build a two-layered hierarchical and interpretable FRBS; then, we improve this by integrating an ontology reasoning process based on SNOMED CT standard ontology. We incorporate FAHP to determine the relative medical importance of each sub-FRBS. The proposed system offers numerous unique and critical improvements regarding the implementation of an accurate, dynamic, semantically intelligent, and interpretable CDSS. The designed system considers the ontology semantic similarity of diabetes complications and symptoms concepts in the fuzzy rules' evaluation process. The frame workwas tested using a real data set, and the results indicate how the proposed system helps physicians and patients to accurately diagnose diabetes mellitus.