Evidence-Based Medicine (EBM) has been an important practice for medical practitioners. However, as the number of medical publications increases dramatically, it is becoming extremely difficult for medical experts to review all the contents available and make an informative treatment plan for their patients. A variety of frameworks, including the PICO framework which is named after its elements (Population, Intervention, Comparison, Outcome), have been developed to enable fine-grained searches, as the first step to faster decision making.

In this work, we propose a novel entity recognition system that identifies PICO entities within medical publications and achieves state-of-the-art performance in the task. This is achieved by the combination of four 2D Convolutional Neural Networks (CNNs) for character feature extraction, and a Highway Residual connection to facilitate deep Neural Network architectures. We further introduce a PICO Statement classifier, that identifies sentences that not only contain all PICO entities but also answer questions stated in PICO. To facilitate this task we also introduce a high quality dataset, manually annotated by medical practitioners. With the combination of our proposed PICO Entity Recognizer and PICO Statement classifier we aim to advance EBM and enable its faster and more accurate practice.

循证医学 (EBM) 一直是医疗从业者的重要实践。然而，随着医学出版物数量的急剧增加，医学专家很难审查所有可用内容并为患者制定信息丰富的治疗计划。已经开发了各种框架，包括以其元素（人口、干预、比较、结果）命名的 PICO 框架，以实现细粒度搜索，作为加快决策制定的第一步。

在这项工作中，我们提出了一种新颖的实体识别系统，可以识别医学出版物中的 PICO 实体并在任务中实现最先进的性能。这是通过结合四个用于字符特征提取的 2D 卷积神经网络 (CNN) 和用于促进深度神经网络架构的公路残差连接来实现的。我们进一步引入了 PICO Statement 分类器，它识别不仅包含所有 PICO 实体而且还回答 PICO 中陈述的问题的句子。为了促进这项任务，我们还引入了一个高质量的数据集，由医生手动注释。通过我们提出的 PICO 实体识别器和 PICO 语句分类器的组合，我们的目标是推进 EBM 并使其更快、更准确地实践。