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Related Experiment Video

Updated: Jul 6, 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

Enhancing recall in automated record screening: A resampling algorithm.

Zhipeng Hou¹, Elizabeth Tipton¹

¹Department of Statistics and Data Science, Northwestern University, Evanston, Illinois, USA.

Research Synthesis Methods

|January 7, 2024

Summary

This summary is machine-generated.

This study introduces a reliable screening prioritization algorithm for literature reviews. The new method ensures a high probability of finding relevant records, improving efficiency in research synthesis.

Keywords:

automatic screening algorithm data mining literature screen machine learning text mining

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Area of Science:

Information Science
Medical Informatics
Bibliometrics

Background:

Literature screening is crucial for systematic reviews but is time-consuming, costly, and error-prone.
Existing screening prioritization methods lack guaranteed reliable performance for practical applications.
The need for robust algorithms to enhance efficiency and accuracy in research synthesis is evident.

Purpose of the Study:

To develop a screening prioritization algorithm with guaranteed reliable performance for practical use in research synthesis.
To ensure the algorithm identifies a high proportion of relevant records with a specified probability.
To create a wrapper algorithm adaptable to existing screening prioritization methods.

Main Methods:

Developed a screening prioritization algorithm based on Cormack and Grossman's target-based method.
Employed sampling with replacement within the algorithm.
Utilized mathematical proofs and probability theory to guarantee performance.
Conducted numerical experiments to evaluate practical performance.

Main Results:

The proposed algorithm demonstrates reliable performance across various scenarios.
Mathematical proofs confirm the algorithm's guaranteed performance.
Numerical experiments validate its effectiveness in practical applications.
The algorithm successfully achieves its objective of reliable record identification.

Conclusions:

The developed screening prioritization algorithm offers reliable performance for real-world research synthesis.
It addresses the limitations of existing methods by providing performance guarantees.
This advancement can significantly improve the efficiency and accuracy of systematic reviews and meta-analyses.