Understanding Clinical Trial Reports: Extracting Medical Entities and Their Relations
Understanding Clinical Trial Reports: Extracting Medical Entities and Their Relations
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发表时间：
2020-10
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通讯作者：
Benjamin E. Nye;Jay DeYoung;Eric P. Lehman;A. Nenkova;I. Marshall;Byron C. Wallace
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作者：
Benjamin E. Nye;Jay DeYoung;Eric P. Lehman;A. Nenkova;I. Marshall;Byron C. Wallace
关于比较治疗效果的最佳证据来自临床试验，其结果在非结构化文章中报告。医学专家必须手动从文章中提取信息来为决策提供信息，这既耗时又昂贵。在这里，我们考虑以下端到端任务：(a) 从描述临床试验的全文文章中提取治疗和结果（实体识别），以及 (b) 推断前者相对于后者的报告结果（关系提取）。我们为此任务引入了新数据，并评估了最近在自然语言处理中的类似任务中取得了最先进结果的模型。然后，我们提出了一种新方法，其动机是试验结果通常如何呈现，其性能优于这些纯粹数据驱动的基线。最后，我们与一家非营利组织对该模型进行了现场评估，旨在识别可能重新用于癌症的现有药物，显示端到端证据提取系统的潜在效用。
The best evidence concerning comparative treatment effectiveness comes from clinical trials, the results of which are reported in unstructured articles. Medical experts must manually extract information from articles to inform decision-making, which is time-consuming and expensive. Here we consider the end-to-end task of both (a) extracting treatments and outcomes from full-text articles describing clinical trials (entity identification) and, (b) inferring the reported results for the former with respect to the latter (relation extraction). We introduce new data for this task, and evaluate models that have recently achieved state-of-the-art results on similar tasks in Natural Language Processing. We then propose a new method motivated by how trial results are typically presented that outperforms these purely data-driven baselines. Finally, we run a fielded evaluation of the model with a non-profit seeking to identify existing drugs that might be re-purposed for cancer, showing the potential utility of end-to-end evidence extraction systems.