释放深度PICO提取的力量：逐步医学NER识别
Unlocking the Power of Deep PICO Extraction: Step-wise Medical NER Identification
摘要：
PICO框架（人群、干预、比较和结局）通常用于制定医学领域的证据。PICO抽取的主要任务是从医学文献中抽取句子并将其分类。然而，在大多数情况下，即使提取的句子已被归类为某个类别，提取的句子中也会存在多个证据。为了解决这个问题，我们提出了一个逐步的疾病命名实体识别（DNER）提取和PICO识别方法。该方法首先将论文标题和摘要中的句子划分为不同的PICO类，然后识别医学实体并将其划分为P类和O类。采用不同的深度学习框架进行实验，实验结果表明，与传统的PICO抽取方法相比，该方法能够获得高性能和细粒度的抽取结果。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
