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Updated: Dec 17, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Machine Learning Assisted Citation Screening for Systematic Reviews.

Anjani Dhrangadhariya1, Roger Hilfiker2, Roger Schaer1

  • 1University of Applied Sciences Western Switzerland (HES-SO), Sierre, Switzerland.

Studies in Health Technology and Informatics
|June 24, 2020
PubMed
Summary

Automating citation screening for systematic reviews (SR) using machine learning (ML) can reduce costs. This research explores ML for narrow SR questions, addressing challenges like class imbalance and overlap.

Keywords:
AutomationMachine learningNatural language processingSystematic reviews

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Area of Science:

  • Information Science
  • Computer Science
  • Medical Informatics

Background:

  • Evidence-based practice relies on up-to-date systematic reviews (SR).
  • Manual citation screening for SRs is labor-intensive and costly.
  • Automating citation screening with machine learning (ML) can improve efficiency.

Purpose of the Study:

  • To investigate the automation of citation screening for SRs with narrow research questions using ML.
  • To analyze the impact of class imbalance and class overlap on ML classifier performance in this context.

Main Methods:

  • Application of machine learning algorithms for citation screening.
  • Evaluation of ML model performance on systematic reviews with narrow research questions.
  • Analysis of class imbalance and class overlap issues.

Main Results:

  • Machine learning shows potential for automating citation screening in narrow SRs.
  • Class imbalance and class overlap present challenges to ML classifier performance.
  • Ongoing research provides insights into optimizing ML for this specific task.

Conclusions:

  • Automating citation screening for narrow systematic reviews using ML is feasible but requires addressing specific challenges.
  • Further research is needed to refine ML approaches for improved accuracy and efficiency in SR screening.