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.

提出了一种基于疾病命名实体识别的 PICO 方法，使用深度学习模型从医学文献中提取出句子并将其分类，并在此基础上识别并分类出 P 和 O，并且通过实验结果展示了该方法可以取得比传统 PICO 提取方法更高的性能和细粒度的提取结果。