Li Zhi-Qing, Fu Zi-Xuan, Li Wen-Jun, Fan Hao, Li Shu-Nan, Wang Xi-Mo, Zhou Peng
Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China.
Tianjin Medical University Eye Hospital, Tianjin 300392, China.
Diagnostics (Basel). 2023 May 26;13(11):1858. doi: 10.3390/diagnostics13111858.
Diabetic macular edema (DME) is a significant complication of diabetes that impacts the eye and is a primary contributor to vision loss in individuals with diabetes. Early control of the related risk factors is crucial to reduce the incidence of DME. Artificial intelligence (AI) clinical decision-making tools can construct disease prediction models to aid in the clinical screening of the high-risk population for early disease intervention. However, conventional machine learning and data mining techniques have limitations in predicting diseases when dealing with missing feature values. To solve this problem, a knowledge graph displays the connection relationships of multi-source and multi-domain data in the form of a semantic network to enable cross-domain modeling and queries. This approach can facilitate the personalized prediction of diseases using any number of known feature data. In this study, we proposed an improved correlation enhancement algorithm based on knowledge graph reasoning to comprehensively evaluate the factors that influence DME to achieve disease prediction. We constructed a knowledge graph based on Neo4j by preprocessing the collected clinical data and analyzing the statistical rules. Based on reasoning using the statistical rules of the knowledge graph, we used the correlation enhancement coefficient and generalized closeness degree method to enhance the model. Meanwhile, we analyzed and verified these models' results using link prediction evaluation indicators. The disease prediction model proposed in this study achieved a precision rate of 86.21%, which is more accurate and efficient in predicting DME. Furthermore, the clinical decision support system developed using this model can facilitate personalized disease risk prediction, making it convenient for the clinical screening of a high-risk population and early disease intervention.
糖尿病性黄斑水肿（DME）是糖尿病的一种严重并发症，会影响眼睛，是糖尿病患者视力丧失的主要原因。早期控制相关风险因素对于降低DME的发病率至关重要。人工智能（AI）临床决策工具可以构建疾病预测模型，以辅助对高危人群进行临床筛查，以便进行早期疾病干预。然而，传统的机器学习和数据挖掘技术在处理缺失特征值时，在疾病预测方面存在局限性。为了解决这个问题，知识图谱以语义网络的形式展示多源多领域数据的关联关系，以实现跨域建模和查询。这种方法可以利用任意数量的已知特征数据促进疾病的个性化预测。在本研究中，我们提出了一种基于知识图谱推理的改进相关增强算法，以综合评估影响DME的因素，从而实现疾病预测。我们通过对收集到的临床数据进行预处理并分析统计规则，构建了一个基于Neo4j的知识图谱。基于知识图谱的统计规则进行推理，我们使用相关增强系数和广义接近度方法对模型进行增强。同时，我们使用链接预测评估指标对这些模型的结果进行分析和验证。本研究提出的疾病预测模型实现了86.21%的准确率，在预测DME方面更加准确和高效。此外，使用该模型开发的临床决策支持系统可以促进个性化疾病风险预测，便于对高危人群进行临床筛查和早期疾病干预。
