Abstract
Background: Researchers in evidence-based medicine cannot keep up with the amounts of both old and newly published primary research articles. Support for the early stages of the systematic review process - searching and screening studies for eligibility - is necessary because it is currently impossible to search for relevant research with precision. Better automated data extraction may not only facilitate the stage of review traditionally labelled 'data extraction', but also change earlier phases of the review process by making it possible to identify relevant research. Exponential improvements in computational processing speed and data storage are fostering the development of data mining models and algorithms. This, in combination with quicker pathways to publication, led to a large landscape of tools and methods for data mining and extraction. Objective: To review published methods and tools for data extraction to (semi)automate the systematic reviewing process. Methods: We propose to conduct a living review. With this methodology we aim to do constant evidence surveillance, bi-monthly search updates, as well as review updates every 6 months if new evidence permits it. In a cross-sectional analysis we will extract methodological characteristics and assess the quality of reporting in our included papers. Conclusions: We aim to increase transparency in the reporting and assessment of automation technologies to the benefit of data scientists, systematic reviewers and funders of health research. This living review will help to reduce duplicate efforts by data scientists who develop data mining methods. It will also serve to inform systematic reviewers about possibilities to support their data extraction.
Highlights
Researchers in evidence-based medicine cannot keep up with the amounts of both old and newly published primary research articles
Aims of this review This review aims to: 1. Review published methods and tools aimed at automating or semi-automating the process of data extraction in the context of a systematic review of medical research studies
We seek to highlight contributions of methods and tools from the perspective of systematic reviewers who wish to useautomation for data extraction: what is the extent of automation?; is it reliable?; and can we identify important caveats discussed in the literature, as well as factors that facilitate the adoption of tools in practice?
Summary
Bond University, Robina, Any reports and responses or comments on the article can be found at the end of the article. Review published methods and tools aimed at automating or semi-automating the process of data extraction in the context of a systematic review of medical research studies. Jonnalagadda et al described methods for data extraction, focusing on PICOs and related fields[4] These reviews present an overview of classical machine learning and NLP methods applied to tasks such as data mining in the field of evidence-based medicine. As well as in every published update, we will present a cross-sectional analysis of the evidence from our searches This analysis will include the characteristics of each reviewed method or tool, as well as a summary of our findings. We will include full text publications that describe an original natural language processing approach to extract data related to systematic reviewing tasks. Any publications related to electronic health reports or mining genetic data will be excluded
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