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A High Recall Classifier for Selecting Articles for MEDLINE Indexing.

Alastair R Rae1, Max E Savery1, James G Mork1

  • 1Lister Hill National Center for Biomedical Communications, National Library of Medicine, Bethesda, MD.

AMIA ... Annual Symposium Proceedings. AMIA Symposium
|April 21, 2020
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Summary

A new machine learning system semi-automates the selection of biomedical articles for MEDLINE (Medical Literature Analysis and Retrieval System Online). This improves efficiency by reducing manual review needs for indexing staff.

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Area of Science:

  • Biomedical Informatics
  • Machine Learning
  • Information Retrieval

Background:

  • MEDLINE (Medical Literature Analysis and Retrieval System Online) is a key bibliographic database for biomedical literature.
  • Manual indexing using Medical Subject Headings (MeSH) is crucial but time-consuming, especially for selectively indexed journals.
  • The increasing burden on indexing staff necessitates more efficient workflows.

Purpose of the Study:

  • To present a machine learning-based system to semi-automate the identification of in-scope articles for MEDLINE.
  • To significantly reduce the time and effort required for manual article selection.
  • To improve the efficiency of the MEDLINE indexing process.

Main Methods:

  • Development of a high-recall machine learning classifier to identify journal articles relevant to MEDLINE.
  • Implementation of a semi-automated system to assist indexing staff in the selection process.
  • Evaluation of the system's performance in reducing manual review workload.

Main Results:

  • The machine learning system achieved a 54% reduction in the number of articles requiring manual review.
  • This translates to an estimated annual saving of approximately 40,000 articles for manual review.
  • The system effectively identifies articles within the scope of MEDLINE.

Conclusions:

  • Machine learning offers a viable solution to streamline the MEDLINE article selection process.
  • Semi-automation significantly reduces the burden on indexing staff, enhancing operational efficiency.
  • The developed system demonstrates substantial time savings and improved workflow for biomedical literature indexing.