论文标题
通过多文件摘要预测临床试验中的干预批准
Predicting Intervention Approval in Clinical Trials through Multi-Document Summarization
论文作者
论文摘要
临床试验为发现新疗法并提高医学知识提供了基本机会。但是，试验结果的不确定性可能导致不可预见的成本和挫折。在这项研究中，我们提出了一种新方法，以预测临床试验中干预的有效性。我们的方法依赖于从文献中有关所研究干预措施中可用的多个文档中产生信息的摘要。具体而言，我们的方法首先收集了与干预有关的PubMed文章的所有摘要。然后，从每个摘要中自动提取有关干预措施有效性的信息的证据句子。根据从摘要中提取的一系列证据句子，构建了有关干预措施的简短摘要。最后，生产的摘要用于培训基于BERT的分类器，以推断干预措施的有效性。为了评估我们提出的方法，我们引入了一个新的数据集，该数据集是临床试验的集合以及相关的PubMed文章。我们的实验证明了产生简短信息摘要的有效性，并使用它们来预测干预的有效性。
Clinical trials offer a fundamental opportunity to discover new treatments and advance the medical knowledge. However, the uncertainty of the outcome of a trial can lead to unforeseen costs and setbacks. In this study, we propose a new method to predict the effectiveness of an intervention in a clinical trial. Our method relies on generating an informative summary from multiple documents available in the literature about the intervention under study. Specifically, our method first gathers all the abstracts of PubMed articles related to the intervention. Then, an evidence sentence, which conveys information about the effectiveness of the intervention, is extracted automatically from each abstract. Based on the set of evidence sentences extracted from the abstracts, a short summary about the intervention is constructed. Finally, the produced summaries are used to train a BERT-based classifier, in order to infer the effectiveness of an intervention. To evaluate our proposed method, we introduce a new dataset which is a collection of clinical trials together with their associated PubMed articles. Our experiments, demonstrate the effectiveness of producing short informative summaries and using them to predict the effectiveness of an intervention.
