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Machine learning to optimize literature screening in medical guideline development.

Wouter Harmsen1, Janke de Groot1, Albert Harkema2

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Summary

Active learning tools can speed up medical guideline development by improving literature screening. However, their performance depends on accurate human input, as noisy labels lead to unreliable machine learning outcomes.

Keywords:
Active learningGuideline developmentMachine learningSystematic reviewing

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Area of Science:

  • Medical Informatics
  • Evidence-Based Medicine
  • Machine Learning

Background:

  • The exponential growth of medical evidence necessitates efficient methods for literature selection in guideline development.
  • Current manual processes for evidence selection are labor-intensive and can hinder the timely updating of medical guidelines.

Purpose of the Study:

  • To evaluate the performance and feasibility of active learning (AL) in supporting the selection of relevant publications for medical guideline development.
  • To investigate the impact of noisy labels on the effectiveness of AL in this context.

Main Methods:

  • A mixed-methods design was employed, evaluating manual literature selection by clinicians across 14 searches.
  • Simulations compared random reading with AL-based screening prioritization.
  • Performance metrics included Work Saved over Sampling at 95% recall (WSS@95) and percentage Relevant Records Found at 10% reading (RRF@10).
  • Average Time to Discovery (ATD) was used to identify potentially noisy labels, with accuracy discussed in a reflective dialogue.

Main Results:

  • Manual title-abstract selection by clinicians showed moderate inter-rater reliability (mean Kappa = 0.50).
  • Active learning demonstrated improved efficiency, with WSS@95 reaching up to 75.76% for full-text inclusion, significantly outperforming manual methods.
  • The performance of AL deteriorated with increased label noise, highlighting the impact of human rater accuracy.

Conclusions:

  • Active learning tools show promise in accelerating literature screening for medical guidelines.
  • The efficacy of AL is critically dependent on the quality of human input; "noisy labels make noisy machine learning."
  • Guideline developers and researchers must ensure accurate labeling to maximize the benefits of AL tools.