电子病历信息检索的自然语言处理与并行计算

Ali Abu Salimeh, Najah Al-shanableh, M. Alzyoud
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引用次数: 0

摘要

在本文中,我们回顾了文献,以寻找适合电子健康的信息检索技术。还讨论了NLP技术已被证明能够从电子病历的非结构化数据中提取有价值的信息。用于搜索自由文本的最佳NLP技术之一是LSI,因为它具有查找语义术语的能力,并且通过查找术语之间的隐藏关系来丰富搜索结果。LSI使用一种称为SVD的数学模型,由于其复杂性和耗尽内存,该模型不能扩展到大量数据,并讨论了LSI的最新应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Natural Language Processing and Parallel Computing for Information Retrieval from Electronic Health Records
In this paper, we review the literature to find suitable information retrieval techniques for EHealth. Also discussed NLP techniques that have been proved their capability to extract valuable information in unstructured data from EHR. One of the best NLP techniques used for searching free text is LSI, due to its capability of finding semantic terms and in rich the search results by finding the hidden relations between terms. LSI uses a mathematical model called SVD, which is not scalable for large amounts of data due to its complexity and exhausts the memory, and a review for recent applications of LSI was discussed.
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