A Comprehensive Evaluation of Biomedical Entity Linking Models.

David Kartchner, Jennifer Deng, Shubham Lohiya, Tejasri Kopparthi, Prasanth Bathala, Daniel Domingo-Fernández, Cassie S Mitchell
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Abstract

Biomedical entity linking (BioEL) is the process of connecting entities referenced in documents to entries in biomedical databases such as the Unified Medical Language System (UMLS) or Medical Subject Headings (MeSH). The study objective was to comprehensively evaluate nine recent state-of-the-art biomedical entity linking models under a unified framework. We compare these models along axes of (1) accuracy, (2) speed, (3) ease of use, (4) generalization, and (5) adaptability to new ontologies and datasets. We additionally quantify the impact of various preprocessing choices such as abbreviation detection. Systematic evaluation reveals several notable gaps in current methods. In particular, current methods struggle to correctly link genes and proteins and often have difficulty effectively incorporating context into linking decisions. To expedite future development and baseline testing, we release our unified evaluation framework and all included models on GitHub at https://github.com/davidkartchner/biomedical-entity-linking.

生物医学实体链接模型的综合评估。
生物医学实体链接(BioEL)是将文档中引用的实体与统一医学语言系统(UMLS)或医学主题词表(MeSH)等生物医学数据库中的条目连接起来的过程。研究的目的是在一个统一的框架下全面评估九种最新的生物医学实体链接模型。我们从以下几个方面对这些模型进行了比较:(1) 准确性;(2) 速度;(3) 易用性;(4) 通用性;(5) 对新本体和数据集的适应性。此外,我们还量化了各种预处理选择(如缩写检测)的影响。系统评估揭示了当前方法中存在的几个显著缺陷。特别是,目前的方法很难正确地连接基因和蛋白质,而且往往难以有效地将上下文纳入连接决策。为了加快未来的开发和基线测试,我们在 GitHub 上发布了统一的评估框架和所有包含的模型,网址是 https://github.com/davidkartchner/biomedical-entity-linking。
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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