The Ohio State University, Columbus, USA.
Amazon Web Services AI, Palo Alto, USA.
Sci Rep. 2022 Mar 18;12(1):4724. doi: 10.1038/s41598-022-08454-z.
Effective and successful clinical trials are essential in developing new drugs and advancing new treatments. However, clinical trials are very expensive and easy to fail. The high cost and low success rate of clinical trials motivate research on inferring knowledge from existing clinical trials in innovative ways for designing future clinical trials. In this manuscript, we present our efforts on constructing the first publicly available Clinical Trials Knowledge Graph, denoted as [Formula: see text]. [Formula: see text] includes nodes representing medical entities in clinical trials (e.g., studies, drugs and conditions), and edges representing the relations among these entities (e.g., drugs used in studies). Our embedding analysis demonstrates the potential utilities of [Formula: see text] in various applications such as drug repurposing and similarity search, among others.
有效的和成功的临床试验对于开发新药和推进新疗法至关重要。然而，临床试验非常昂贵，且容易失败。临床试验的高成本和低成功率促使人们以创新的方式从现有临床试验中推断知识，以设计未来的临床试验。在本文中，我们介绍了构建第一个公开可用的临床试验知识图的努力，称为[公式：见文本]。[公式：见文本]包含表示临床试验中的医学实体（例如研究、药物和病症）的节点，以及表示这些实体之间关系的边（例如研究中使用的药物）。我们的嵌入分析表明了[公式：见文本]在药物重定位和相似性搜索等各种应用中的潜在用途。
