利用自然语言处理和机器学习进行标题-摘要半自动筛选。

IF 6.3 4区 医学 Q1 MEDICINE, GENERAL & INTERNAL Systematic Reviews Pub Date : 2024-11-01 DOI:10.1186/s13643-024-02688-w
Maximilian Pilz, Samuel Zimmermann, Juliane Friedrichs, Enrica Wördehoff, Ulrich Ronellenfitsch, Meinhard Kieser, Johannes A Vey
{"title":"利用自然语言处理和机器学习进行标题-摘要半自动筛选。","authors":"Maximilian Pilz, Samuel Zimmermann, Juliane Friedrichs, Enrica Wördehoff, Ulrich Ronellenfitsch, Meinhard Kieser, Johannes A Vey","doi":"10.1186/s13643-024-02688-w","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Title-abstract screening in the preparation of a systematic review is a time-consuming task. Modern techniques of natural language processing and machine learning might allow partly automatization of title-abstract screening. In particular, clear guidance on how to proceed with these techniques in practice is of high relevance.</p><p><strong>Methods: </strong>This paper presents an entire pipeline how to use natural language processing techniques to make the titles and abstracts usable for machine learning and how to apply machine learning algorithms to adequately predict whether or not a publication should be forwarded to full text screening. Guidance for the practical use of the methodology is given.</p><p><strong>Results: </strong>The appealing performance of the approach is demonstrated by means of two real-world systematic reviews with meta analysis.</p><p><strong>Conclusions: </strong>Natural language processing and machine learning can help to semi-automatize title-abstract screening. Different project-specific considerations have to be made for applying them in practice.</p>","PeriodicalId":22162,"journal":{"name":"Systematic Reviews","volume":null,"pages":null},"PeriodicalIF":6.3000,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11529237/pdf/","citationCount":"0","resultStr":"{\"title\":\"Semi-automated title-abstract screening using natural language processing and machine learning.\",\"authors\":\"Maximilian Pilz, Samuel Zimmermann, Juliane Friedrichs, Enrica Wördehoff, Ulrich Ronellenfitsch, Meinhard Kieser, Johannes A Vey\",\"doi\":\"10.1186/s13643-024-02688-w\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Title-abstract screening in the preparation of a systematic review is a time-consuming task. Modern techniques of natural language processing and machine learning might allow partly automatization of title-abstract screening. In particular, clear guidance on how to proceed with these techniques in practice is of high relevance.</p><p><strong>Methods: </strong>This paper presents an entire pipeline how to use natural language processing techniques to make the titles and abstracts usable for machine learning and how to apply machine learning algorithms to adequately predict whether or not a publication should be forwarded to full text screening. Guidance for the practical use of the methodology is given.</p><p><strong>Results: </strong>The appealing performance of the approach is demonstrated by means of two real-world systematic reviews with meta analysis.</p><p><strong>Conclusions: </strong>Natural language processing and machine learning can help to semi-automatize title-abstract screening. Different project-specific considerations have to be made for applying them in practice.</p>\",\"PeriodicalId\":22162,\"journal\":{\"name\":\"Systematic Reviews\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":6.3000,\"publicationDate\":\"2024-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11529237/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Systematic Reviews\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1186/s13643-024-02688-w\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MEDICINE, GENERAL & INTERNAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Systematic Reviews","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s13643-024-02688-w","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MEDICINE, GENERAL & INTERNAL","Score":null,"Total":0}
引用次数: 0

摘要

背景:编写系统综述时的标题-摘要筛选是一项耗时的工作。现代自然语言处理和机器学习技术可使标题-摘要筛选部分自动化。特别是,如何在实践中使用这些技术的明确指导具有重要意义:本文介绍了如何使用自然语言处理技术使标题和摘要可用于机器学习,以及如何应用机器学习算法充分预测出版物是否应转入全文筛选的整个流程。此外,还给出了该方法的实际使用指南:结果:通过两篇真实世界的系统综述和元分析,展示了该方法的吸引力:自然语言处理和机器学习有助于实现标题-摘要筛选的半自动化。在实际应用中,必须针对具体项目做出不同的考虑。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Semi-automated title-abstract screening using natural language processing and machine learning.

Background: Title-abstract screening in the preparation of a systematic review is a time-consuming task. Modern techniques of natural language processing and machine learning might allow partly automatization of title-abstract screening. In particular, clear guidance on how to proceed with these techniques in practice is of high relevance.

Methods: This paper presents an entire pipeline how to use natural language processing techniques to make the titles and abstracts usable for machine learning and how to apply machine learning algorithms to adequately predict whether or not a publication should be forwarded to full text screening. Guidance for the practical use of the methodology is given.

Results: The appealing performance of the approach is demonstrated by means of two real-world systematic reviews with meta analysis.

Conclusions: Natural language processing and machine learning can help to semi-automatize title-abstract screening. Different project-specific considerations have to be made for applying them in practice.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Systematic Reviews
Systematic Reviews Medicine-Medicine (miscellaneous)
CiteScore
8.30
自引率
0.00%
发文量
241
审稿时长
11 weeks
期刊介绍: Systematic Reviews encompasses all aspects of the design, conduct and reporting of systematic reviews. The journal publishes high quality systematic review products including systematic review protocols, systematic reviews related to a very broad definition of health, rapid reviews, updates of already completed systematic reviews, and methods research related to the science of systematic reviews, such as decision modelling. At this time Systematic Reviews does not accept reviews of in vitro studies. The journal also aims to ensure that the results of all well-conducted systematic reviews are published, regardless of their outcome.
期刊最新文献
The efficacy and safety of ketorolac for postoperative pain management in lumbar spine surgery: a meta-analysis of randomized controlled trials. Semi-automated title-abstract screening using natural language processing and machine learning. Stroke patient and stakeholder engagement (SPSE): concepts, definitions, models, implementation strategies, indicators, and frameworks-a systematic scoping review. The distribution of work-related musculoskeletal disorders among nurses in sub-Saharan Africa: a scoping review. Toward a whole-of-virtual school framework for promoting student physical activity: a scoping review protocol.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1