Suppr超能文献

生物医学中的语义标注:现状

Semantic annotation in biomedicine: the current landscape.

作者信息

Jovanović Jelena, Bagheri Ebrahim

机构信息

Department of Software Engineering, University of Belgrade, 154 Jove Ilica Street, Belgrade, Serbia.

Department of Electrical Engineering, Ryerson University, 245 Church Street, Toronto, Canada.

出版信息

J Biomed Semantics. 2017 Sep 22;8(1):44. doi: 10.1186/s13326-017-0153-x.

Abstract

The abundance and unstructured nature of biomedical texts, be it clinical or research content, impose significant challenges for the effective and efficient use of information and knowledge stored in such texts. Annotation of biomedical documents with machine intelligible semantics facilitates advanced, semantics-based text management, curation, indexing, and search. This paper focuses on annotation of biomedical entity mentions with concepts from relevant biomedical knowledge bases such as UMLS. As a result, the meaning of those mentions is unambiguously and explicitly defined, and thus made readily available for automated processing. This process is widely known as semantic annotation, and the tools that perform it are known as semantic annotators.Over the last dozen years, the biomedical research community has invested significant efforts in the development of biomedical semantic annotation technology. Aiming to establish grounds for further developments in this area, we review a selected set of state of the art biomedical semantic annotators, focusing particularly on general purpose annotators, that is, semantic annotation tools that can be customized to work with texts from any area of biomedicine. We also examine potential directions for further improvements of today's annotators which could make them even more capable of meeting the needs of real-world applications. To motivate and encourage further developments in this area, along the suggested and/or related directions, we review existing and potential practical applications and benefits of semantic annotators.

摘要

生物医学文本数量众多且结构松散,无论是临床内容还是研究内容,都给有效利用此类文本中存储的信息和知识带来了巨大挑战。用机器可理解的语义对生物医学文档进行注释有助于实现基于语义的高级文本管理、编目、索引和搜索。本文重点关注使用诸如统一医学语言系统(UMLS)等相关生物医学知识库中的概念对生物医学实体提及进行注释。这样一来,这些提及的含义就得到了明确无误的定义,从而便于进行自动化处理。这个过程被广泛称为语义注释,执行该过程的工具被称为语义注释器。

在过去的十几年里,生物医学研究界在生物医学语义注释技术的开发上投入了大量精力。为了为该领域的进一步发展奠定基础,我们回顾了一组精选的生物医学语义注释器的最新技术,特别关注通用注释器,即可以定制以处理来自生物医学任何领域文本的语义注释工具。我们还研究了当今注释器进一步改进的潜在方向,以使它们更能满足实际应用的需求。为了激励和鼓励沿着建议的和/或相关方向在该领域进一步发展,我们回顾了语义注释器现有的和潜在的实际应用及益处。

相似文献

1
Semantic annotation in biomedicine: the current landscape.
J Biomed Semantics. 2017 Sep 22;8(1):44. doi: 10.1186/s13326-017-0153-x.
2
RysannMD: A biomedical semantic annotator balancing speed and accuracy.
J Biomed Inform. 2017 Jul;71:91-109. doi: 10.1016/j.jbi.2017.05.016. Epub 2017 May 26.
3
SIFR annotator: ontology-based semantic annotation of French biomedical text and clinical notes.
BMC Bioinformatics. 2018 Nov 6;19(1):405. doi: 10.1186/s12859-018-2429-2.
4
Large scale biomedical texts classification: a kNN and an ESA-based approaches.
J Biomed Semantics. 2016 Jun 16;7:40. doi: 10.1186/s13326-016-0073-1.
5
Semantic biomedical resource discovery: a Natural Language Processing framework.
BMC Med Inform Decis Mak. 2015 Sep 30;15:77. doi: 10.1186/s12911-015-0200-4.
6
Automated ontology generation framework powered by linked biomedical ontologies for disease-drug domain.
Comput Methods Programs Biomed. 2018 Oct;165:117-128. doi: 10.1016/j.cmpb.2018.08.010. Epub 2018 Aug 16.
7
Building a comprehensive syntactic and semantic corpus of Chinese clinical texts.
J Biomed Inform. 2017 May;69:203-217. doi: 10.1016/j.jbi.2017.04.006. Epub 2017 Apr 9.
9
Cross-lingual semantic annotation of biomedical literature: experiments in Spanish and English.
Bioinformatics. 2020 Mar 1;36(6):1872-1880. doi: 10.1093/bioinformatics/btz853.
10
Semantator: semantic annotator for converting biomedical text to linked data.
J Biomed Inform. 2013 Oct;46(5):882-93. doi: 10.1016/j.jbi.2013.07.003. Epub 2013 Jul 15.

引用本文的文献

1
MetaTron: advancing biomedical annotation empowering relation annotation and collaboration.
BMC Bioinformatics. 2024 Mar 14;25(1):112. doi: 10.1186/s12859-024-05730-9.
2
A substring replacement approach for identifying missing IS-A relations in SNOMED CT.
Proceedings (IEEE Int Conf Bioinformatics Biomed). 2022 Dec;2022:2611-2618. doi: 10.1109/bibm55620.2022.9995595. Epub 2023 Jan 2.
3
Knowledge Graph Embeddings for ICU readmission prediction.
BMC Med Inform Decis Mak. 2023 Jan 19;23(1):12. doi: 10.1186/s12911-022-02070-7.
4
Identifying Datasets for Cross-Study Analysis in dbGaP using PhenX.
Sci Data. 2022 Sep 1;9(1):532. doi: 10.1038/s41597-022-01660-4.
6
Data Integration Challenges for Machine Learning in Precision Medicine.
Front Med (Lausanne). 2022 Jan 25;8:784455. doi: 10.3389/fmed.2021.784455. eCollection 2021.
7
MedTAG: a portable and customizable annotation tool for biomedical documents.
BMC Med Inform Decis Mak. 2021 Dec 18;21(1):352. doi: 10.1186/s12911-021-01706-4.
8
Improving broad-coverage medical entity linking with semantic type prediction and large-scale datasets.
J Biomed Inform. 2021 Sep;121:103880. doi: 10.1016/j.jbi.2021.103880. Epub 2021 Aug 12.
9
ECO-CollecTF: A Corpus of Annotated Evidence-Based Assertions in Biomedical Manuscripts.
Front Res Metr Anal. 2021 Jul 13;6:674205. doi: 10.3389/frma.2021.674205. eCollection 2021.
10
Leveraging network analysis to evaluate biomedical named entity recognition tools.
Sci Rep. 2021 Jun 29;11(1):13537. doi: 10.1038/s41598-021-93018-w.

本文引用的文献

1
v3NLP Framework: Tools to Build Applications for Extracting Concepts from Clinical Text.
EGEMS (Wash DC). 2016 Aug 11;4(3):1228. doi: 10.13063/2327-9214.1228. eCollection 2016.
2
Allocation of Physician Time in Ambulatory Practice: A Time and Motion Study in 4 Specialties.
Ann Intern Med. 2016 Dec 6;165(11):753-760. doi: 10.7326/M16-0961. Epub 2016 Sep 6.
3
TaggerOne: joint named entity recognition and normalization with semi-Markov Models.
Bioinformatics. 2016 Sep 15;32(18):2839-46. doi: 10.1093/bioinformatics/btw343. Epub 2016 Jun 9.
6
NOBLE - Flexible concept recognition for large-scale biomedical natural language processing.
BMC Bioinformatics. 2016 Jan 14;17:32. doi: 10.1186/s12859-015-0871-y.
7
8
Semantic biomedical resource discovery: a Natural Language Processing framework.
BMC Med Inform Decis Mak. 2015 Sep 30;15:77. doi: 10.1186/s12911-015-0200-4.
9
Sophia: A Expedient UMLS Concept Extraction Annotator.
AMIA Annu Symp Proc. 2014 Nov 14;2014:467-76. eCollection 2014.
10
A multilingual gold-standard corpus for biomedical concept recognition: the Mantra GSC.
J Am Med Inform Assoc. 2015 Sep;22(5):948-56. doi: 10.1093/jamia/ocv037. Epub 2015 May 6.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验