论文标题
自定义知识图嵌入以改善临床研究建议
Customizing Knowledge Graph Embedding to Improve Clinical Study Recommendation
论文作者
论文摘要
使用知识图嵌入从临床试验中推断知识是一个新兴领域。但是，针对不同用例的自定义图形嵌入仍然是一个重大挑战。我们提出了Custom2Vec，这是一种算法框架，可以通过将用户偏好纳入培训嵌入方式来自定义图形嵌入。它通过添加自定义节点和从单独的信息检索方法的手动审查结果得出的链接来捕获用户偏好。我们提出了一个联合学习目标，以保留原始网络结构，同时结合用户的自定义注释。我们假设自定义培训可以改善用户指望的预测，例如，在链接预测任务中。我们证明了Custom2VEC对与非小细胞肺癌（NSCLC）相关的临床试验的有效性，并具有两种自定义方案：建议评估PD-1抑制剂的免疫肿瘤学试验，并探索将新疗法与新疗法和标准护理相比的类似试验。结果表明，自定义2VEC培训比传统培训方法更好。我们的方法是一种自定义知识图嵌入并实现更准确的建议和预测的新颖方法。
Inferring knowledge from clinical trials using knowledge graph embedding is an emerging area. However, customizing graph embeddings for different use cases remains a significant challenge. We propose custom2vec, an algorithmic framework to customize graph embeddings by incorporating user preferences in training the embeddings. It captures user preferences by adding custom nodes and links derived from manually vetted results of a separate information retrieval method. We propose a joint learning objective to preserve the original network structure while incorporating the user's custom annotations. We hypothesize that the custom training improves user-expected predictions, for example, in link prediction tasks. We demonstrate the effectiveness of custom2vec for clinical trials related to non-small cell lung cancer (NSCLC) with two customization scenarios: recommending immuno-oncology trials evaluating PD-1 inhibitors and exploring similar trials that compare new therapies with a standard of care. The results show that custom2vec training achieves better performance than the conventional training methods. Our approach is a novel way to customize knowledge graph embeddings and enable more accurate recommendations and predictions.
