Abstract
Background: Researchers in evidence-based medicine cannot keep up with the amounts of both old and newly published primary research articles. Conducting and updating of systematic reviews is time-consuming. In practice, data extraction is one of the most complex tasks in this process. Exponential improvements in computational processing speed and data storage are fostering the development of data extraction models and algorithms. This, in combination with quicker pathways to publication, led to a large landscape of tools and methods for data extraction tasks. 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 monthly search updates, as well as bi-annual review updates 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 machine learning 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 extraction methods. It will also serve to inform systematic reviewers about possibilities to support their data extraction.
Highlights
Research on systematic reviewautomation sits at the interface between evidence-based medicine and data science
Review size and the number of authors varied between the analysed reviews, the authors concluded that supporting the reviewing process with technological means is important in order to save thousands of personal working hours of trained and specialised staff[1]
Aims of this review This review aims to: 1. Review published methods and tools for PICO data extraction toautomate the systematic reviewing process
Summary
Background Research on systematic review (semi)automation sits at the interface between evidence-based medicine and data science. Review published methods and tools for PICO data extraction to (semi)automate the systematic reviewing process. Related research We have identified three publications involving reviews of tools and methods, a document providing overviews and guidelines relevant to our topic, and an ongoing effort to characterise published tools for different parts of the systematic reviewing process with respect to interoperability and workflow integration. A further review of the same year described methods for data extraction, focusing on PICOs and related fields[4] These reviews present an overview of classical machine learning 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. A comprehensive list with data fields of interest can be found in the supplementary material for this protocol
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