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


Citation Style Language JSON Export

{
  "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"
    }
  ], 
  "type": "paper-conference", 
  "id": "2574710"
}
29
18
views
downloads
All versions This version
Views 2929
Downloads 1818
Data volume 1.8 MB1.8 MB
Unique views 2525
Unique downloads 1616

Share

Cite as