Neural relevance model using similarities with elite documents for effective clinical decision support
Neural relevance model using similarities with elite documents for effective clinical decision support
复制标题
利用与精英文档的相似性的神经相关性模型来提供有效的临床决策支持
DOI：
10.1504/ijdmb.2018.10015098
复制
发表时间：
2018-08
影响因子：
0.3
通讯作者：
Le Sun
中科院分区：
文献类型：
--
作者：
Yanhua Ran;Ben He;Kai Hui;Jungang Xu;Le Sun
临床决策支持（CDS）被认为是一个信息检索（IR）任务，其中医疗记录用于检索全文生物医学文章，以满足医生的信息需求，旨在更好的医疗解决方案。最近的尝试通过将神经IR方法用于CDS来引入深度学习的进步，然而，其中仅对文档-查询关系进行建模，导致非最佳结果，因为医疗记录几乎不能反映相关生物医学文章中包含的信息，而相关生物医学文章通常更长。因此，除了文档查询关系，我们提出了一个神经相关性模型（DNRM）的基础上的相似性，一组精英文件，解决信息不匹配，利用相关文章的内容作为一个完整的图片给定的医疗记录。具体来说，我们的DNRM模型评估一个文档相对于一个查询，并在同一时间查询的几个伪相关的文档，捕捉从两个部分与前馈网络的相互作用。在标准文本检索会议（TREC）CDS跟踪数据集上的实验结果证实了所提出的DNRM模型的上级性能。
Clinical Decision Support (CDS) is regarded as an information retrieval (IR) task, where medical records are used to retrieve full-text biomedical articles to satisfy the information needs from physicians, aiming at better medical solutions. Recent attempts have introduced the advances of deep learning by employing neural IR methods for CDS, where, however, only the document-query relationship is modelled, resulting in non-optimal results in that a medical record can barely reflect the information included in a relevant biomedical article which is usually much longer. Therefore, in addition to the document-query relationship, we propose a neural relevance model (DNRM) based on similarities to a set of elite documents, addressing the information mismatch by utilising the content of relevant articles as a complete picture of the given medical record. Specifically, our DNRM model evaluates a document relative to a query and to several pseudo relevant documents for the query at the same time, capturing the interactions from both parts with a feed forward network. Experimental results on the standard Text REtrieval Conference (TREC) CDS track dataset confirm the superior performance of the proposed DNRM model.