Zhang Xiang, Geng Ping, Zhang Tengteng, Lu Qian, Gao Peng, Mei Jing
IEEE J Biomed Health Inform. 2020 Apr 3;PP. doi: 10.1109/JBHI.2020.2984704.
Evidence-Based Medicine (EBM) aims to apply the best available evidence gained from scientific methods to clinical decision making. A generally accepted criterion to formulate evidence is to use the PICO framework, where PICO stands for Problem/Population, Intervention, Comparison, and Outcome. Automatic extraction of PICO-related sentences from medical literature is crucial to the success of many EBM applications. In this work, we present our Aceso system, which automatically generates PICO-based evidence summaries from medical literature. In Aceso , we adopt an active learning paradigm, which helps to minimize the cost of manual labeling and to optimize the quality of summarization with limited labeled data. An UMLS2Vec model is proposed to learn a vector representation of medical concepts in UMLS , and we fuse the embedding of medical knowledge with textual features in summarization. The evaluation shows that our approach is better on identifying PICO sentences against state-of-the-art studies and outperforms baseline methods on producing high-quality evidence summaries.
循证医学（EBM）旨在将科学方法获得的最佳可用证据应用于临床决策。制定证据的一个普遍接受的标准是使用PICO框架，其中PICO代表问题/人群、干预措施、对照和结果。从医学文献中自动提取与PICO相关的句子对于许多循证医学应用的成功至关重要。在这项工作中，我们展示了我们的Aceso系统，该系统可从医学文献中自动生成基于PICO的证据摘要。在Aceso中，我们采用主动学习范式，这有助于将人工标注的成本降至最低，并在有限的标注数据下优化摘要的质量。我们提出了一种UMLS2Vec模型来学习统一医学语言系统（UMLS）中医学概念的向量表示，并在摘要中融合医学知识的嵌入和文本特征。评估表明，我们的方法在识别PICO句子方面优于现有研究，并且在生成高质量证据摘要方面优于基线方法。
