论文标题
根据增强知识图，完善医学诊断的诊断路径
Refining Diagnosis Paths for Medical Diagnosis based on an Augmented Knowledge Graph
论文作者
论文摘要
医学诊断是对患者可能患有一组症状和观察结果预测该疾病的过程。这需要广泛的专家知识，特别是在涵盖各种疾病时。这些知识可以在知识图中编码 - 包括疾病，症状和诊断路径。由于知识本身及其编码都不完整，因此使用其他信息来完善知识图有助于医生做出更好的预测。同时，要在医院部署，诊断必须是可以解释且透明的。在本文中，我们提出了一种使用医学知识图中诊断路径的方法。我们表明，可以使用带有RDF2VEC的潜在表示可以完善这些图，而最终诊断仍然可以解释。使用固有的和基于专家的评估，我们表明基于嵌入的预测方法有益于使用其他有效条件来完善图表。
Medical diagnosis is the process of making a prediction of the disease a patient is likely to have, given a set of symptoms and observations. This requires extensive expert knowledge, in particular when covering a large variety of diseases. Such knowledge can be coded in a knowledge graph -- encompassing diseases, symptoms, and diagnosis paths. Since both the knowledge itself and its encoding can be incomplete, refining the knowledge graph with additional information helps physicians making better predictions. At the same time, for deployment in a hospital, the diagnosis must be explainable and transparent. In this paper, we present an approach using diagnosis paths in a medical knowledge graph. We show that those graphs can be refined using latent representations with RDF2vec, while the final diagnosis is still made in an explainable way. Using both an intrinsic as well as an expert-based evaluation, we show that the embedding-based prediction approach is beneficial for refining the graph with additional valid conditions.
