基于主要发现的临床病例报告检索的多样化

Mengqi Luo, Fengchang Yu, Haihua Chen
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引用次数: 0

摘要

临床病例报告是生物医学文献中的“目击者”,提供了一种有价值的、独特的、尽管嘈杂且未充分利用的证据。主要发现是撰写这些报告的原因。基于主查找的案件报告检索为用户方便地获取目击证据信息提供了途径。然而,用户的检索需求往往是模糊和多样的,传统的基于相似度的检索机制不能满足用户的不同需求。本文对基于主要发现的病例报告检索结果多样化进行了研究。首先,采用比较主要查找内容的四种相似度度量进行初步结果排序;其次,采用两种隐式重排序算法和两种显式重排序算法实现结果多样化。实验结果表明,我们使用的方法在重新排序结果中提高了子主题覆盖率(CR@ X%),证明了我们的研究工作在提高结果多样化程度方面的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Result Diversification in Clinical Case Reports Retrieval based on Main Finding
Clinical case reports are the ‘eyewitness’ in biomedical literature and provide a valuable, unique, albeit noisy and underutilized type of evidence. Main finding is the reason for writing up the reports. Main finding based case reports retrieval provides way for user to conveniently access information of eyewitness evidence. However, user retrieval requirements are often ambiguous and diverse, traditional similarity based retrieval mechanism cannot meet different needs of users. Here, we conduct research of result diversification in case reports retrieval based on main finding. First, four similarity measurements for comparing main finding contents are used for initial result ranking; second, two implicit reranking algorithms and two explicit reranking algorithms are applied for result diversification. Experimental result showed that the methods we used had improved sub-topics coverage rate (CR@ X%) in re-ranking result, which proved the effectiveness of our research work for improving result diversification degree.
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