机器学习与深度学习模型在医学主题词索引中的比较

Lukman Heryawan, Arif Bukhori
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引用次数: 0

摘要

MeSH代表医学主题标题。MeSH是对生物医学文献中的书籍和文章进行编目的彻底掌握。MeSH作为一本字典,在生物医学领域方便搜索和检索信息。目前,人类索引器使用MeSH词汇手动索引生物医学文献中的文章和书籍。使用人工索引器的问题是,索引过程需要很长时间,而且成本很高。因此,开发的索引是自动化的,这是在本研究中使用基于机器学习和监督深度学习的预测器的MeSH词汇表开发的。研究发现,在预测指数化方面,深度学习指数化模型的F1-Score优于机器学习作为基准模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparison of Machine Learning and Deep Learning model for Medical Subject Headings Indexation
MeSH stands for Medical Subject Heading. MeSH is a thorough mastery for cataloging books and articles in the biomedical literature. MeSH works as a dictionary that facilitates searching and retrieving information in the biomedical realm. Currently, a human indexer manually indexes articles and books in biomedical literature using the MeSH vocabulary. The problem with using a human indexer is that the indexation process takes a long time and is expensive. Therefore, the indexation developed is automated, which is developed in this study using MeSH vocabulary with predictors based on machine learning and supervised deep learning. The study found that the F1-Score of the deep learning indexation model was superior compared to the machine learning used as the baseline model in predicting the indexation.
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