探索医学实体识别在临床信息检索中的有效性

J. Cogley, N. Stokes, J. Carthy
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引用次数: 7

摘要

医学和临床文本数据集的增长促进了对存储、检索和提取相关数据方法的研究兴趣。近年来,共享任务和更全面的数据共享协议在跨越自然语言处理(NLP)和信息检索(IR)的研究领域取得了进一步的发展,以帮助医疗保健领域。通常,NLP应用,如医疗实体识别(MER),是在提高红外系统性能的背景下被激发的。在本文中,我们研究了在各自领域共享任务的背景下,MER在临床检索系统中的应用。也就是说,我们的目标是为以前非结构化的临床报告和查询集添加结构。我们评估了MER在查询集上的性能,突出了在临床设置中构建查询的问题。此外,我们评估了检索数据集上结构化查询的性能。我们发现,虽然结构化查询提高了包含许多术语依赖关系的复杂查询的性能,但在临床文本中发现的更大的语言差异问题也必须得到解决。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Exploring the effectiveness of medical entity recognition for clinical information retrieval
The growth of medical and clinical textual datasets has fostered research interests in methods for storing, retrieving and extracting of pertinent data. In more recent years, shared tasks and more comprehensive data sharing agreements have seen a further growth in the research area spanning Natural Language Processing (NLP) and Information Retrieval (IR) to aid the world of healthcare. Frequently NLP applications such as Medical Entity Recognition (MER), are motivated within the context of improving IR system performance. In this paper, we investigate the application of MER to a clinical retrieval system in the context of shared tasks in the respective areas. Namely, we aim to add structure to previously unstructured clinical reports and query sets. We evaluate the performance of MER on the query set, highlighting issues in constructing queries in a clinical setting. Further to this, we evaluate the performance of structuring queries on a retrieval dataset. We find that while structuring queries improves performance on complex queries that contain many term dependencies, there is a larger issue of linguistic variation found in clinical texts that must also be addressed.
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