Schmidt, Lena;
Finnerty Mutlu, Ailbhe N;
Elmore, Rebecca;
Olorisade, Babatunde K;
Thomas, James;
Higgins, Julian PT;
(2023)
Data extraction methods for systematic review (semi)automation: Update of a living systematic review [version 2; peer review: 3 approved].
F1000Research
, 10
, Article 401. 10.12688/f1000research.51117.2.
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Abstract
Background: The reliable and usable (semi)automation of data extraction can support the field of systematic review by reducing the workload required to gather information about the conduct and results of the included studies. This living systematic review examines published approaches for data extraction from reports of clinical studies. Methods: We systematically and continually search PubMed, ACL Anthology, arXiv, OpenAlex via EPPI-Reviewer, and the dblp computer science bibliography. Full text screening and data extraction are conducted within an open-source living systematic review application created for the purpose of this review. This living review update includes publications up to December 2022 and OpenAlex content up to March 2023. Results: 76 publications are included in this review. Of these, 64 (84%) of the publications addressed extraction of data from abstracts, while 19 (25%) used full texts. A total of 71 (93%) publications developed classifiers for randomised controlled trials. Over 30 entities were extracted, with PICOs (population, intervention, comparator, outcome) being the most frequently extracted. Data are available from 25 (33%), and code from 30 (39%) publications. Six (8%) implemented publicly available tools Conclusions: This living systematic review presents an overview of (semi)automated data-extraction literature of interest to different types of literature review. We identified a broad evidence base of publications describing data extraction for interventional reviews and a small number of publications extracting epidemiological or diagnostic accuracy data. Between review updates, trends for sharing data and code increased strongly: in the base-review, data and code were available for 13 and 19% respectively, these numbers increased to 78 and 87% within the 23 new publications. Compared with the base-review, we observed another research trend, away from straightforward data extraction and towards additionally extracting relations between entities or automatic text summarisation. With this living review we aim to review the literature continually.
| Type: | Article |
|---|---|
| Title: | Data extraction methods for systematic review (semi)automation: Update of a living systematic review [version 2; peer review: 3 approved] |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.12688/f1000research.51117.2 |
| Publisher version: | https://doi.org/10.12688/f1000research.51117.2 |
| Language: | English |
| Additional information: | Copyright: © 2023 Schmidt L et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
| Keywords: | Data Extraction, Natural Language Processing, Reproducibility, Systematic Reviews, Text Mining |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Education UCL > Provost and Vice Provost Offices > School of Education > UCL Institute of Education UCL > Provost and Vice Provost Offices > School of Education > UCL Institute of Education > IOE - Social Research Institute |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10179731 |
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