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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    "description": "<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>", 
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    "title": "Data Extraction and Synthesis in Systematic Reviews of Diagnostic Test Accuracy: A Corpus for Automating and Evaluating the Process", 
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    "publication_date": "2018-11-05", 
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        "affiliation": "LIMSI, CNRS", 
        "name": "Norman, Christopher"
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        "affiliation": "AMC, University of Amsterdam", 
        "name": "Leeflang, Mariska"
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      "acronym": "Proc AMIA Annu Symp", 
      "dates": "2018", 
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