论文标题
解锁Deep Pico提取的力量：逐步医学识别
Unlocking the Power of Deep PICO Extraction: Step-wise Medical NER Identification
论文作者
论文摘要
PICO框架（人口，干预，比较和结果）通常用于在医疗领域中制定证据。 PICO提取的主要任务是从医学文献中提取句子并将其分类为每个类。但是，在大多数情况下，即使已将其分类为某个类别，提取句子中也会有多个证据。为了解决这个问题，我们提出了一种名为实体识别（DNER）提取和PICO识别方法的渐进疾病。使用我们的方法，首先将纸张标题和摘要中的句子分类为不同类别的PICO，然后将医疗实体鉴定并分类为P和O。使用了不同种类的深度学习框架，实验结果表明，我们的方法将获得高性能和良好的颗粒提取结果与常规PICO提取工作相比。
The PICO framework (Population, Intervention, Comparison, and Outcome) is usually used to formulate evidence in the medical domain. The major task of PICO extraction is to extract sentences from medical literature and classify them into each class. However, in most circumstances, there will be more than one evidences in an extracted sentence even it has been categorized to a certain class. In order to address this problem, we propose a step-wise disease Named Entity Recognition (DNER) extraction and PICO identification method. With our method, sentences in paper title and abstract are first classified into different classes of PICO, and medical entities are then identified and classified into P and O. Different kinds of deep learning frameworks are used and experimental results show that our method will achieve high performance and fine-grained extraction results comparing with conventional PICO extraction works.
