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

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{
"publisher": "Zenodo",
"DOI": "10.5281/zenodo.2574710",
"title": "Data Extraction and Synthesis in Systematic Reviews of Diagnostic Test Accuracy: A Corpus for Automating and Evaluating the Process",
"issued": {
"date-parts": [
[
2018,
11,
5
]
]
},
"abstract": "<p>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&ndash;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.</p>",
"author": [
{
"family": "Norman, Christopher"
},
{
"family": "Leeflang, Mariska"
},
{
"family": "N\u00e9v\u00e9ol, Aur\u00e9lie"
}
],
"id": "2574710",
"event-place": "San Fransisco",
"type": "paper-conference",
"event": "Proceedings of the AMIA Annual Symposium (Proc AMIA Annu Symp)"
}
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