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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    <subfield code="d">2018</subfield>
    <subfield code="g">Proc AMIA Annu Symp</subfield>
    <subfield code="a">Proceedings of the AMIA Annual Symposium</subfield>
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    <subfield code="a">Norman, Christopher</subfield>
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    <subfield code="a">Data Extraction and Synthesis in Systematic Reviews of Diagnostic Test Accuracy: A Corpus for Automating and Evaluating the Process</subfield>
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    <subfield code="a">Methods in Research on Research</subfield>
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    <subfield code="a">Other (Open)</subfield>
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    <subfield code="a">&lt;p&gt;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&amp;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.&lt;/p&gt;</subfield>
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