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Data extraction methods for systematic review (semi)automation: A living review protocol.
Lena Schmidt1, Babatunde K Olorisade1, Luke A McGuinness1
1Bristol Medical School, University of Bristol, Bristol, BS8 2PS, UK.
F1000Research
|August 1, 2020
Summary
This living review examines data extraction tools to automate systematic reviews. It aims to improve transparency and reduce duplicated efforts in evidence-based medicine research.
Area of Science:
- Health Research
- Evidence-Based Medicine
- Data Science
Background:
- The volume of published research overwhelms evidence-based medicine researchers.
- Automated data extraction can improve precision in searching and screening studies for systematic reviews.
- Advancements in computational power drive the development of data mining and extraction tools.
Purpose of the Study:
- To review existing methods and tools for data extraction.
- To assess the potential for (semi)automation of the systematic reviewing process.
Main Methods:
- A living review methodology will be employed.
- Bi-monthly search updates and 6-month review updates are planned.
- Cross-sectional analysis of methodological characteristics and reporting quality of included papers.
Main Results:
- This section is to be populated upon completion of the review.
- The review will identify and categorize current data extraction tools and methods.
- Quality of reporting for automation technologies will be assessed.
Conclusions:
- The living review aims to enhance transparency in reporting and assessing automation technologies.
- It will help data scientists avoid duplicated efforts in developing data mining methods.
- Systematic reviewers will be informed about available tools to support data extraction.
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