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A Category Detection Method for Evidence-Based Medicine

机译：一种基于循证医学的类别检测方法

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Evidence-Based Medicine (EBM) gathers evidence by analyzing large databases of medical literatures and retrieving relevant clinical thematic texts. However, the abstracts of medical articles generally show the themes of clinical practice, populations, research methods and experimental results of the thesis in an unstructurized manner, rendering inefficient retrieval of medical evidence. Abstract sentences contain contextual information, and there are complex semantic and grammatical correlations between them, making its classification different from that of independent sentences. This paper proposes a category detection algorithm based on Hierarchical Multi-connected Network (HMcN), regarding the category detection of EBM as a matter of classification of sequential sentences. The algorithm contains multiple structures: (1) The underlying layer produces a sentence vector by combining the pre-trained language model with Bi-directional Long Short Term Memory Network (Bi-LSTM), and applies a multi-layered self-attention structure to the sentence vector so as to work out the internal dependencies of the sentences. (2) The upper layer uses the multi-connected Bi-LSTMs model to directly read the original input sequence to add the contextual information for the sentence vector in the abstract. (3) The top layer optimizes the tag sequence by means of the conditional random field (CRF) model. The extensive experiments on public datasets have demonstrated that the performance of the HMcN model in medical category detection is superior to that of the state-of-the-art text classification method, and the F1 value has increased by 0.4%-0.9%.
机译：基于证据的医学（EBM）通过分析大型医学文献和检索相关临床专题文本的大型数据库来聚集证据。然而，医学文章的摘要通常以不合理的方式展示论文的临床实践，人口，研究方法和实验结果的主题，呈现低效检索医学证据。抽象句包含上下文信息，它们之间存在复杂的语义和语法相关性，使其分类与独立句子不同。本文提出了一种基于分层多连接网络（HMCN）的类别检测算法，关于EBM的类别检测作为顺序句子的分类。该算法包含多个结构：（1）底层通过将预先训练的语言模型与双向长短短期内存网络（Bi-LSTM）组合来产生句子矢量，并应用多层自我关注结构句子矢量以解决句子的内部依赖性。 （2）上层使用多连接的BI-LSTM模型直接读取原始输入序列，以添加摘要中句子向量的上下文信息。 （3）顶层通过条件随机字段（CRF）模型来优化标签序列。公共数据集的广泛实验表明，医学类别检测中HMCN模型的性能优于最先进的文本分类方法，F1值增加了0.4％-0.9％。

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