Zhao Zhixue, Thomas James, Kell Gregory, Stansfield Claire, Clowes Mark, Graziosi Sergio, Brunton Jeff, Marshall Iain James, Stevenson Mark
Department of Computer Science, University of Sheffield, Sheffield S10 2TN, United Kingdom.
EPPI Centre, UCL Social Research Institute, Institute of Education, University College London, London WC1E 6BT, United Kingdom.
JAMIA Open. 2024 Dec 13;7(4):ooae139. doi: 10.1093/jamiaopen/ooae139. eCollection 2024 Dec.
In public health, access to research literature is critical to informing decision-making and to identify knowledge gaps. However, identifying relevant research is not a straightforward task since public health interventions are often complex, can have positive and negative impacts on health inequalities and are applied in diverse and rapidly evolving settings. We developed a "living" database of public health research literature to facilitate access to this information using Natural Language Processing tools.
Classifiers were identified to identify the study design (eg, cohort study or clinical trial) and relationship to factors that may be relevant to inequalities using the PROGRESS-Plus classification scheme. Training data were obtained from existing MEDLINE labels and from a set of systematic reviews in which studies were annotated with PROGRESS-Plus categories.
Evaluation of the classifiers showed that the study type classifier achieved average precision and recall of 0.803 and 0.930, respectively. The PROGRESS-Plus classification proved more challenging with average precision and recall of 0.608 and 0.534. The FAIR database uses information provided by these classifiers to facilitate access to inequality-related public health literature.
Previous work on automation of evidence synthesis has focused on clinical areas rather than public health, despite the need being arguably greater.
The development of the FAIR database demonstrates that it is possible to create a publicly accessible and regularly updated database of public health research literature focused on inequalities. The database is freely available from https://eppi.ioe.ac.uk/eppi-vis/Fair.
NIHR133603.
在公共卫生领域，获取研究文献对于为决策提供信息和识别知识差距至关重要。然而，识别相关研究并非易事，因为公共卫生干预措施往往复杂，可能对健康不平等产生积极和消极影响，且应用于多样且快速变化的环境中。我们开发了一个公共卫生研究文献的“动态”数据库，以利用自然语言处理工具促进对这些信息的获取。
使用PROGRESS-Plus分类方案确定分类器，以识别研究设计（如队列研究或临床试验）以及与可能与不平等相关因素的关系。训练数据来自现有的MEDLINE标签以及一组系统评价，其中研究已用PROGRESS-Plus类别进行注释。
对分类器的评估表明，研究类型分类器的平均精确率和召回率分别达到0.803和0.930。PROGRESS-Plus分类的挑战更大，平均精确率和召回率分别为0.608和0.534。FAIR数据库利用这些分类器提供的信息，促进对与不平等相关的公共卫生文献的获取。
尽管对证据综合自动化的需求可能更大，但此前的工作主要集中在临床领域而非公共卫生领域。
FAIR数据库的开发表明，有可能创建一个专注于不平等问题的、可公开访问且定期更新的公共卫生研究文献数据库。该数据库可从https://eppi.ioe.ac.uk/eppi-vis/Fair免费获取。
NETSCC识别号：NIHR133603。
