Finding Accessible Inequalities Research in Public Health (the FAIR Database)
This study seeks to develop methods to apply machine learning and Natural Language Processing approaches to support the review, assessment, evaluation and summarisation of large volumes of public health research to support decision making. It will develop and apply automatic methods for identifying information about inequalities, study types and common themes mentioned within large volumes of public health research. The output of these techniques will be made available through an online tool containing a continuously updated repository of public health research. Users will also be able to upload their own data for processing and download results.
The key research question we aim to address is:
Can text mining be used to maintain a ‘living’ database of public health research, including information about topics, methods and inequalities?
We will address this research question by developing a ‘living’ database of public health research in collaboration with public health decision-makers, researchers, and patients and the public. The database will be populated by identifying public health records from the >240 million records in Microsoft Academic Graph and will be a ‘living’ database, as it will be updated every two weeks with newly published research.
The database will go live in the autumn of 2021.
Project staff include:
This study/project is funded by the National Institute for Health Research (NIHR) Public Health Research Programme (NIHR133603). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care.