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: University of Manchester
• 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
• 起止时间: 2014 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FL01078X%2F1
• 关键词: Supporting; Evidence; based; Public; Health

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.

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。其发展目前涉及首先搜索，然后筛选和综合大量文献的证据。与系统性综述不同，EBPH综述需要对文献中的相关信息进行动态和多维的观察，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。由于关键信息可能难以定位，而且鉴于EBPH问题的复杂性，实际上很难理解，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隐藏。该项目将通过探索新的研究方法来解决这些局限性，该方法将联合收割机文本挖掘和机器学习相结合，以产生新颖的搜索，同时为公共卫生评论筛选工具。文本挖掘方法将从非结构化数据中自动发现知识，机器学习将支持将提取的信息优先排序和排序为有意义的主题。文本挖掘和机器学习方法的结合将减少制作公共卫生评论的负担，这将更快地完成，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可靠的审查，从而改善整个卫生部门的决策。该项目将通过与NICE公共卫生中心的密切互动来了解整个项目，该中心还将在实施新的搜索同时进行筛选试点系统的基础上进行定性和定量评估。将对与预防危险和有害饮酒及过度饮酒有关的非传染性疾病的审查进行评价。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渠道影响文件，与英国和国际上的各种关键EBPH利益攸关方进行接触。

项目成果

Quantifying the Informativeness of Similarity Measurements

• 发表时间: 2017-07
• 期刊: J. Mach. Learn. Res.
• 作者: A. Brockmeier;Tingting Mu;S. Ananiadou;J. Y. Goulermas

Topic detection using paragraph vectors to support active learning in systematic reviews.

• 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

A cross-lingual similarity measure for detecting biomedical term translations.

• 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

Supporting the annotation of chronic obstructive pulmonary disease (COPD) phenotypes with text mining workflows.

• 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

Mapping Phenotypic Information in Heterogeneous Textual Sources to a Domain-Specific Terminological Resource.

• DOI: 10.1371/journal.pone.0162287
• 发表时间: 2016
• 期刊: PloS one
• 影响因子: 3.7
• 作者: Alnazzawi N;Thompson P;Ananiadou S
• 通讯作者: Ananiadou S