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Supporting Evidence-based Public Health Interventions using Text Mining

Supporting Evidence-based Public Health Interventions using Text Mining
使用文本挖掘支持循证公共卫生干预措施
批准号:
MR/L01078X/1
负责人:
Sophia Ananiadou
金额:
$83.55万
依托单位:
依托单位国家:
英国
项目类别:
Research Grant
财政年份:
2014
资助国家:
英国
项目状态:
已结题
起止时间:
2014 至 --

项目摘要

项目成果

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中文摘要
翻译
循证公共卫生(EBPH)审查在公共卫生政策、实践和指导中发挥着核心作用。它们的发展目前包括首先搜索,然后从大量文献中筛选和合成证据。与系统审查不同,EBPH审查需要对文献中的相关信息进行动态和多维的查看,而不依赖于先验的研究问题。因此,EBPH审查是一个耗时和资源密集型的过程,可能需要一年多的时间才能完成。鉴于EBPH问题的复杂性质,关键信息可能很难找到,而且确实很难理解,因此干预措施、疾病、人口和结果之间的多重原因和相互关系可能仍然是隐藏的。这个项目将通过探索新的研究方法来解决这些限制,这些方法结合了文本挖掘和机器学习来产生新的搜索,同时为公共卫生审查筛选工具。文本挖掘方法将自动从非结构化数据中发现知识,而机器学习将支持对提取的信息进行优先排序和排序,使其成为有意义的主题。文本挖掘和机器学习方法的结合将减少编制公共卫生审查的负担,这些审查将更快地完成,从而满足政策和实践的时间表,并提高其成本效益。它们还允许进行更及时和更可靠的审查,从而改善整个卫生部门的决策。将通过与NICE公共卫生中心的密切互动在整个过程中向该项目通报情况,该中心还将在实施新的边搜索边筛选试点系统的基础上进行定性和定量评估。对与预防危险有害饮酒和过度饮酒有关的非传染性疾病的审查进行评估。此外,鉴于EBPH审查在国家和国际上的重要性,该项目制定了一份多链影响路径文件,以便与英国和国际上各种关键的EBPH利益攸关方接触。
英文摘要
Evidence-based public health (EBPH) reviews play a central role in public health policy, practice and guidance. Their development currently involves first searching, then screening and synthesizing evidence from the vast amount of literature. Unlike systematic reviews, EBPH reviews require dynamic and multidimensional views of relevant information from the literature, without relying on a priori research questions. As a result, EBPH reviewing is a time consuming and resource intensive process that can take more than a year to complete. Since crucial information can be difficult to locate, and indeed understand given the complex nature of EBPH problems, the multiple causes and interrelations between interventions, diseases, populations and outcomes can remain hidden. This project will address these limitations by exploring new research methods, which combine text mining and machine learning to produce novel search while screening tools for public health reviews. Text mining methods will discover automatically knowledge from unstructured data and machine learning will support the prioritisation and ranking of the extracted information into meaningful topics. The combination of text mining and machine learning methods will reduce the burden of producing public health reviews which will be completed more quickly, thus meeting policy and practice timescales and increasing their cost efficiency. They also allow more timely and reliable reviews, thus improving decision making across the health sector. The project will be informed throughout by close interaction with the Centre of Public Health at NICE , who will also carry out qualitative and quantitative evaluation based on the implementation of a novel search while screening pilot system. Evaluation will be carried out on reviews related with non-communicable diseases related with prevention of hazardous and harmful drinking and excessive alcohol consumption. Moreover, given the national and international importance of EBPH reviewing, the project has developed a multistrand pathways to impact document to engage with a variety of key EBPH stakeholders both in the UK and internationally.
期刊论文(10)
专著(0)
科研奖励(0)
会议论文
DOI: --
发表时间: 2017-07
期刊: J. Mach. Learn. Res.
影响因子: --
作者: [A. Brockmeier;Tingting Mu;S. Ananiadou;J. Y. Goulermas]
通讯作者: A. Brockmeier;Tingting Mu;S. Ananiadou;J. Y. Goulermas
DOI: 10.1016/j.jbi.2016.06.001
发表时间: 2016-08
期刊: Journal of biomedical informatics
影响因子: 4.5
作者: [Hashimoto K, Kontonatsios G, Miwa M, Ananiadou S]
通讯作者: Ananiadou S
DOI: 10.1371/journal.pone.0126196
发表时间: 2015
期刊: PloS one
影响因子: 3.7
作者: [Bollegala D, Kontonatsios G, Ananiadou S]
通讯作者: Ananiadou S
DOI: 10.1186/s13326-015-0004-6
发表时间: 2015
期刊: Journal of biomedical semantics
影响因子: 1.9
作者: [Fu X, Batista-Navarro R, Rak R, Ananiadou S]
通讯作者: Ananiadou S
共 8 条
    Japan Partnering Award. Text mining and bioinformatics platforms for metabolic pathway modelling.
    • 批准号:
      BB/P025684/1
    • 项目类别:
      Research Grant
    • 资助金额:
      $5.07万
    • 财政年份:
      2017
    • 负责人:
      Sophia Ananiadou
    • 依托单位:
    Enriching Metabolic PATHwaY models with evidence from the literature (EMPATHY)
    • 批准号:
      BB/M006891/1
    • 项目类别:
      Research Grant
    • 资助金额:
      $75.68万
    • 财政年份:
      2015
    • 负责人:
      Sophia Ananiadou
    • 依托单位:
    Mining the History of Medicine
    • 批准号:
      AH/L00982X/1
    • 项目类别:
      Research Grant
    • 资助金额:
      $33.31万
    • 财政年份:
      2014
    • 负责人:
      Sophia Ananiadou
    • 依托单位:
    Automated Biological Event Extraction from the Literature for Drug Discovery
    • 批准号:
      BB/G013160/1
    • 项目类别:
      Research Grant
    • 资助金额:
      $36.76万
    • 财政年份:
      2009
    • 负责人:
      Sophia Ananiadou
    • 依托单位:
    海外基金