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Related Experiment Video

Updated: Oct 14, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Applying machine classifiers to update searches: Analysis from two case studies.

Claire Stansfield¹, Gillian Stokes¹, James Thomas¹

¹EPPI-Centre, UCL Social Research Institute, University College London, London, UK.

Research Synthesis Methods

|November 8, 2021

Summary

This summary is machine-generated.

Machine classifiers can significantly reduce manual screening of public health research records for update searches. These tools achieved high recall and substantial screening reductions, saving valuable research time.

Keywords:

information retrieval supervised machine learning systematic reviews as topic update search

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Last Updated: Oct 14, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

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Published on: November 9, 2011

Area of Science:

Information Science
Public Health Research

Background:

Manual screening of citation records is time-consuming, especially for large update searches.
Machine classifiers offer a potential solution to automate and expedite the screening process.

Purpose of the Study:

To evaluate the performance and implementation of machine classifiers for update searches in public health research.
To assess the impact of different training datasets on classifier performance and screening reduction.

Main Methods:

Two case studies were conducted to evaluate machine classifiers.
Study one compared classifier performance using different training data against a manual screening gold standard.
Study two applied a trained classifier to rank update search results and measured screening time.

Main Results:

Custom-built classifiers achieved over 93% recall, reducing screening workload by 41%–74% in study one.
In study two, classifier application reduced screening volume by 61%, potentially saving over 25 hours of screening time.
Classifier performance was influenced by the training data used.

Conclusions:

Machine classifiers are feasible for reducing screening workload in public health update searches.
Key considerations include training data selection, setting stopping thresholds, and workflow integration.
While effective, some limitations on recall should be noted.