BACKGROUND Machine learning can assist with multiple tasks during systematic reviews to facilitate the rapid retrieval of relevant references during screening and to identify and extract information relevant to the study characteristics, which include the PICO elements of patient/population, intervention, comparator, and outcomes. The latter requires techniques for identifying and categorising fragments of text, known as named entity recognition. METHODS A publicly available corpus of PICO annotations on biomedical abstracts is used to train a named entity recognition model, which is implemented as a recurrent neural network. This model is then applied to a separate collection of abstracts for references from systematic reviews within biomedical and health domains. The occurrences of words tagged in the context of specific PICO contexts are used as additional features for a relevancy classification model. Simulations of the machine learning-assisted screening are used to evaluate the work saved by the relevancy model with and without the PICO features. Chi-squared and statistical significance of positive predicted values are used to identify words that are more indicative of relevancy within PICO contexts. RESULTS Inclusion of PICO features improves the performance metric on 15 of the 20 collections, with substantial gains on certain systematic reviews. Examples of words whose PICO context are more precise can explain this increase. CONCLUSIONS Words within PICO tagged segments in abstracts are predictive features for determining inclusion. Combining PICO annotation model into the relevancy classification pipeline is a promising approach. The annotations may be useful on their own to aid users in pinpointing necessary information for data extraction, or to facilitate semantic search.

中文翻译：

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通过 PICO 识别提高参考优先级。

背景机器学习可以在系统评价期间协助完成多项任务，以促进在筛选期间快速检索相关参考文献，并识别和提取与研究特征相关的信息，包括患者/人群、干预、比较器和结果的 PICO 元素。后者需要识别和分类文本片段的技术，称为命名实体识别。方法 一个公开可用的生物医学摘要上的 PICO 注释语料库用于训练命名实体识别模型，该模型被实现为循环神经网络。然后将该模型应用于单独的摘要集合，以供生物医学和健康领域内的系统评价参考。在特定 PICO 上下文的上下文中标记的单词的出现被用作相关性分类模型的附加特征。机器学习辅助筛选的模拟用于评估具有和不具有 PICO 特征的相关性模型保存的工作。正预测值的卡方和统计显着性用于识别在 PICO 上下文中更能指示相关性的词。结果 包含 PICO 功能提高了 20 个集合中的 15 个的性能指标，在某些系统评价上取得了显着收益。PICO 上下文更精确的单词示例可以解释这种增加。结论 摘要中 PICO 标记片段中的单词是确定包含的预测特征。将 PICO 注释模型结合到相关性分类管道中是一种很有前途的方法。注释本身可能有用，可帮助用户确定数据提取所需的信息，或促进语义搜索。