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本文引用的文献

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Representation of behaviour change interventions and their evaluation: Development of the Upper Level of the Behaviour Change Intervention Ontology.行为改变干预措施的呈现及其评估：行为改变干预本体论上层的发展
Wellcome Open Res. 2021 Jan 6;5:123. doi: 10.12688/wellcomeopenres.15902.2. eCollection 2020.
2
Toward systematic review automation: a practical guide to using machine learning tools in research synthesis.迈向系统评价自动化：在研究综合中使用机器学习工具的实用指南。
Syst Rev. 2019 Jul 11;8(1):163. doi: 10.1186/s13643-019-1074-9.
3
Information Extraction of Behavior Change Intervention Descriptions.行为改变干预描述的信息提取
AMIA Jt Summits Transl Sci Proc. 2019 May 6;2019:182-191. eCollection 2019.
4
Unsupervised Information Extraction from Behaviour Change Literature.从行为改变文献中进行无监督信息提取。
Stud Health Technol Inform. 2018;247:680-684.
5
Optimising the value of the evidence generated in implementation science: the use of ontologies to address the challenges.优化实施科学中产生的证据的价值：使用本体论来应对挑战。
Implement Sci. 2017 Nov 14;12(1):131. doi: 10.1186/s13012-017-0660-2.
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Analysis of the time and workers needed to conduct systematic reviews of medical interventions using data from the PROSPERO registry.利用PROSPERO注册库的数据，分析对医学干预措施进行系统评价所需的时间和人员。
BMJ Open. 2017 Feb 27;7(2):e012545. doi: 10.1136/bmjopen-2016-012545.
7
Extracting PICO Sentences from Clinical Trial Reports using .使用……从临床试验报告中提取PICO句子
J Mach Learn Res. 2016;17.
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Reviews: Rapid! Rapid! Rapid! …and systematic.综述：迅速！迅速！迅速！……且系统。
Syst Rev. 2015 Jan 14;4(1):4. doi: 10.1186/2046-4053-4-4.
9
Living systematic reviews: an emerging opportunity to narrow the evidence-practice gap.实时系统评价：缩小证据-实践差距的新契机。
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10
Identifying scientific artefacts in biomedical literature: the Evidence Based Medicine use case.识别生物医学文献中的科学伪品：循证医学用例。
J Biomed Inform. 2014 Jun;49:159-70. doi: 10.1016/j.jbi.2014.02.006. Epub 2014 Feb 14.

从行为科学随机对照试验中提取和预测知识：以戒烟为例。

Knowledge Extraction and Prediction from Behavior Science Randomized Controlled Trials: A Case Study in Smoking Cessation.

机构信息

IBM Research Europe, Dublin, Ireland.

University College London, UK.

出版信息

AMIA Annu Symp Proc. 2021 Jan 25;2020:253-262. eCollection 2020.

PMID:33936397
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8075460/
Abstract

Due to the fast pace at which randomized controlled trials are published in the health domain, researchers, consultants and policymakers would benefit from more automatic ways to process them by both extracting relevant information and automating the meta-analysis processes. In this paper, we present a novel methodology based on natural language processing and reasoning models to 1) extract relevant information from RCTs and 2) predict potential outcome values on novel scenarios, given the extracted knowledge, in the domain of behavior change for smoking cessation.

摘要

由于健康领域随机对照试验的发布速度很快，研究人员、顾问和政策制定者将受益于通过提取相关信息和自动化荟萃分析过程来更自动地处理这些试验的方法。在本文中，我们提出了一种基于自然语言处理和推理模型的新方法，用于 1）从 RCT 中提取相关信息，以及 2）根据提取的知识，在戒烟行为改变领域，对新场景下的潜在结果值进行预测。