A Literature Review on Machine Learning Based Medical Information Retrieval Systems

Akhil Gudivada, Nasseh Tabrizi
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引用次数: 11

Abstract

As many fields progress with the assistance of cognitive computing, the field of health care is also adapting, providing many benefits to all users. However, advancements in this area are hindered by several challenges such as the void between user queries and the knowledge base, query mismatches, and range of domain knowledge in users. In this paper, we present existing methodologies as well as look into existing real-life applications that are used in the medical field today. We also look into specific challenges and techniques that can be used to overcome these barriers, specifically related to cognitive computing in the medical domain. Future information retrieval (IR) models that can be tailored specifically for medically intensive applications which can handle large amounts of data are explored as well. The purpose of this paper is to give the reader an in-depth understanding of artificial intelligence being used in the medical field today, as well as future possibilities in the domain.
基于机器学习的医学信息检索系统研究综述
随着许多领域在认知计算的帮助下取得进展,医疗保健领域也在适应,为所有用户提供了许多好处。然而,这一领域的进展受到一些挑战的阻碍,例如用户查询和知识库之间的空白、查询不匹配以及用户的领域知识范围。在本文中,我们介绍了现有的方法,并研究了目前在医学领域使用的现有现实应用。我们还研究了可用于克服这些障碍的具体挑战和技术,特别是与医学领域的认知计算相关的挑战和技术。未来的信息检索(IR)模型,可以专门为医疗密集型应用程序,可以处理大量的数据进行了探索。本文的目的是让读者深入了解人工智能在当今医疗领域的应用,以及该领域未来的可能性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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