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Updated: Jul 5, 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

Systematic review using a spiral approach with machine learning.

Amirhossein Saeidmehr¹, Piers David Gareth Steel², Faramarz F Samavati³

¹Computer Science Department, University of Calgary, 2500 University Dr., Calgary, Canada. amir.saeidmehr@cpsc.ucalgary.ca.

Systematic Reviews

|January 17, 2024

Summary

This summary is machine-generated.

A new spiral machine learning approach significantly improves systematic review screening efficiency. This method enhances processing speed and accuracy, making literature reviews more manageable amidst growing academic data.

Keywords:

Active learning Machine learning Systematic review Technology-assisted review

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Area of Science:

Information Science
Computer Science
Bibliometrics

Background:

The exponential growth of academic literature necessitates efficient methods for systematic reviews.
Current machine learning applications in systematic reviews often follow traditional, human-centric workflows (e.g., PRISMA), limiting their potential.
Optimizing machine learning integration is crucial for managing the increasing volume of research publications.

Purpose of the Study:

To evaluate a novel spiral, alternating approach for machine learning-assisted systematic review screening.
To compare the spiral approach against traditional sequential methods across various machine learning configurations.
To determine the effectiveness of the spiral approach in improving the efficiency and manageability of systematic reviews.

Main Methods:

Simulations were conducted on three datasets under 360 distinct conditions.
Tested variables included algorithmic classifiers, feature extraction techniques (e.g., TF-IDF), prioritization rules (e.g., maximum probability), and data types.
The spiral approach involved intermittent full-text screening interspersed with title/abstract screening.

Main Results:

The spiral processing approach, particularly with logistic regression, TF-IDF vectorization, and maximum probability prioritization, consistently outperformed traditional methods.
Significant improvements, up to 90%, were observed, especially in datasets with fewer eligible articles.
This optimized machine learning strategy enhances the feasibility of the screening component in systematic reviews.

Conclusions:

The spiral machine learning approach offers a substantial advancement over conventional methodologies for systematic review screening.
This method is projected to keep the screening component of systematic reviews achievable for the next 10-20 years.
Further research can refine these machine learning techniques for broader application in scientific literature analysis.