PICO-DS

Automatic extraction of valuable, structured evidence from the exponentially growing clinical trial literature can help physicians practice evidence-based medicine quickly and accurately. However, current research on evidence extraction has been limited by the lack of generalization ability on various clinical topics and the high cost of manual annotation. In this work, we address these challenges by constructing a PICO-based evidence dataset PICO-DS, covering five clinical topics. This dataset was automatically labeled by a distant supervision based on our proposed textual similarity algorithm called ROUGE-Hybrid. PICO-DS is a distant supervision dataset that includes 24,909 samples across 5 medical topics. Each sample has its corresponding PICO label. We according to the PICO framework defines four types of tags: P on behalf of the Patient/Population/Problem, I on behalf of Intervention/Comparision, O on behalf of the Outcome, N for NA, which does not belong to the above three kinds of classification.

自动从指数级增长的临床试验文献中提取具有价值的结构化证据，有助于医师快速、准确地实践循证医学。然而，当前关于证据提取的研究受到多种临床主题泛化能力不足以及人工标注高昂成本的制约。在本研究中，我们通过构建基于PICO框架的证据数据集PICO-DS，涵盖五个临床主题，来应对这些挑战。该数据集通过基于我们提出的ROUGE-Hybrid文本相似度算法的远端监督机制进行自动标注。PICO-DS是一个包含24,909个样本，涵盖5个医学主题的远端监督数据集。每个样本都对应一个PICO标签。我们根据PICO框架定义了四种类型的标签：P代表患者/人群/问题，I代表干预/比较，O代表结果，N代表NA，即不属于上述三种分类。