Abstract

A knowledge graph is a structured representation of data that can express entity and relational knowledge. More attention has been paid to the study of a clinical knowledge graph, especially in the field of chronic diseases. However, knowledge graph construction is based mainly on electronic medical records and other data sources, and the authority of the constructed knowledge graph presents some problems. Therefore, regarding the quality of evidence, this study, in combination with experimental research on system evaluation and meta-analysis presents some new information, On the basis of evidence-based medicine (EBM), the secondary results of systematic evaluation and meta-analyses of social, psychological, and behavioral aspects were extracted as data for the core nodes and edges of a knowledge graph to construct a graph of type 2 diabetes (T2D) and its complications. In this study, relevant life-style evidence that are factors for the risk of diabetic retinopathy (DR), diabetic nephropathy (DN), diabetic foot (DF), and diabetic depression (DD), and the results of several of the relevant clinical test, including bariatric surgery, myopia, lipid-lowering drugs, lipid-lowering drug duration, blood glucose control, disease course, glycosylated hemoglobin, fasting blood glucose, hypertension, sex, smoking and other common lifestyle characteristics were finally extracted. The evidence-based knowledge graph of the DM complications was constructed by extracting relevant disease, risk factors, risk outcomes, and other diabetes entities and the strength of the data for the odds ratio (OR) or relative risk (RR) correlations from clinical evidence. Moreover, the risk prediction models constructed using a logistic model were incorporated into the knowledge graph to visualize the risk score of DM complications for each user. In short, the EBM-powered construction of the knowledge graph could provide high-quality information to support decisions for the prevention and control of diabetes and its complications.

摘要

知识图是可以表示实体知识和关系知识的数据的结构化表示。已经更加关注临床知识图的研究，尤其是在慢性疾病领域。然而，知识图的构建主要基于电子病历和其他数据源，并且所构建的知识图的权威性存在一些问题。因此，关于证据的质量，本研究结合系统评估和荟萃分析的实验研究提供了一些新信息，在循证医学（EBM）的基础上，系统评估和荟萃分析的次要结果社会，心理，提取行为和行为方面的数据作为知识图的核心节点和边缘的数据，以构建2型糖尿病（T2D）及其并发症的图。在这项研究中，相关的生活方式证据是糖尿病性视网膜病变（DR），糖尿病性肾病（DN），糖尿病足（DF）和糖尿病性抑郁症（DD）风险的因素，以及一些相关临床结果的结果测试包括抽脂手术，近视，降脂药，降脂药持续时间，血糖控制，病程，糖基化血红蛋白，空腹血糖，高血压，性别，吸烟和其他常见的生活方式特征。通过提取相关疾病，风险因素，风险结果，以及其他糖尿病实体以及来自临床证据的比值比（OR）或相对风险（RR）相关性的数据强度。此外，将使用逻辑模型构建的风险预测模型合并到知识图中，以可视化每个用户的DM并发症的风险评分。简而言之，由EBM支持的知识图的构建可以提供高质量的信息，以支持预防和控制糖尿病及其并发症的决策。