基于医疗知识图谱的并发症辅助诊断

为了实现文本描述中的快速并发症的准确预判,该文结合知识图谱、表示学习、深度神经网络等方法构建了一个并发症辅助诊断模型。该模型首先构建医疗领域的知识图谱,并通过知识表示模型对医疗领域知识进行编码,结合患者主诉文本获取患者症状实体的表示向量,再将患者主诉表示向量和指标表示向量通过CNN-DNN网络对并发症进行辅助诊断。实验选取了糖尿病的3种并发症: 高血压、糖尿病肾病和糖尿病视网膜病变作为测试。该文模型的准确率对比支持向量机、随机森林和单独的深度神经网络在高血压、糖尿病肾病和糖尿病视网膜病变上分别提高了5%、5%、14%和27%、6%、9%,说明该文模型能够充分融合医疗知识图谱和深度学习技术,对提高并发症的诊断起到积极作用。

Medical Knowledge Graph Based Auxiliary Diagnosis of Complications

Aiming at the accurate and rapid diagnosis of complications, this paper proposes an auxiliary diagnosis model based on knowledge graph, representation model and deep neural network. Firstly, a medical knowledge graph is constructed, which is represented by the vector for each entity and relation. Then according to chief complaints of the patients, the symptom entities are detected and again represented by vectors. Eventually, the above two kind of vectors are input to the CNN-DNN classification model joint with the index representation to diagnose the complications. The experiment chooses three complications of diabetes: hypertension, diabetic nephropathy and diabetic retinopathy. The accuracy of the proposed model is improved by 5%, 5%, 14% compared with the classical machine learning methods, respectively; and 27%, 6%, 9% higher than that of previous DNN model.