Conference paper Open Access
Data Extraction and Synthesis in Systematic Reviews of Diagnostic Test Accuracy: A Corpus for Automating and Evaluating the Process
Norman, Christopher;
Leeflang, Mariska;
Névéol, Aurélie
Background: Systematic reviews are critical for obtaining accurate estimates of diagnostic test accuracy, yet these require extracting information buried in free text articles, which is often laborious. Objective: We create a dataset describing the data extraction and synthesis processes in 63 DTA systematic reviews, and demonstrate its utility by using it to replicate the data synthesis in the original reviews. Method: We construct our dataset using a custom automated extraction pipeline complemented with manual extraction, verification, and post-editing. We evaluate using manual assessment by two annotators and by comparing against data extracted from source files. Results: The constructed dataset contains 5,848 test results for 1,354 diagnostic tests from 1,738 diagnostic studies. We observe an extraction error rate of 0.06–0.3%. Conclusions: This constitutes the first dataset describing the later stages of the DTA systematic review process, and is intended to be useful for automating or evaluating the process.
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- Publication date:
- November 5, 2018
- DOI:
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- Grants:
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European Commission:
- MIROR - Methods in Research on Research (676207)
- Meeting:
- Proceedings of the AMIA Annual Symposium (Proc AMIA Annu Symp), San Fransisco, 2018
- Communities:
- License (for files):
- Other (Open)