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Related Experiment Video

Updated: Jun 22, 2026

A Novel Method for Involving Women of Color at High Risk for Preterm Birth in Research Priority Setting

A Novel Method for Involving Women of Color at High Risk for Preterm Birth in Research Priority Setting

Published on: January 12, 2018

Cross-topic learning for work prioritization in systematic review creation and update.

Aaron M Cohen¹, Kyle Ambert, Marian McDonagh

¹Department of Medical Informatics, Clinical Epidemiology, School of Medicine, Oregon Health & Science University, 3181 S. W. Sam Jackson Park Road, Mail Code: BICC, Portland, OR 97239-3098, USA. cohenaa@ohsu.edu

Journal of the American Medical Informatics Association : JAMIA

|July 2, 2009

Summary

A hybrid machine learning approach significantly improves automated document ranking for systematic reviews, especially when topic-specific data is limited. This method enhances work prioritization for researchers conducting literature reviews.

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A Novel Method for Involving Women of Color at High Risk for Preterm Birth in Research Priority Setting

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Published on: June 6, 2020

Area of Science:

Computer Science
Information Science
Medical Informatics

Background:

Systematic reviews (SRs) require efficient literature prioritization.
Machine learning (ML) offers automated solutions for ranking articles.
Current ML systems may lack sufficient topic-specific training data.

Purpose of the Study:

To evaluate a hybrid ML approach for automated document ranking in SRs.
To determine if combining topic-specific and non-topic data improves ranking accuracy.
To enhance work prioritization for experts conducting systematic reviews.

Main Methods:

A support vector machine (SVM) algorithm was employed.
A test collection used annotated reference files from 24 drug class SRs.
The hybrid approach combined varying fractions of topic-specific data with data from other SR topics, compared against baseline and non-topic systems.

Main Results:

The hybrid system improved mean AUC by 20% over the baseline when topic-specific data was scarce.
The hybrid system outperformed the baseline across all tested data fractions.
Performance was significantly better than the non-topic system, especially with larger fractions of topic-specific data.

Conclusions:

Automated literature prioritization using hybrid ML aids experts in organizing SRs.
Future work includes incorporating more topic-specific data sources.
Embedding the algorithm into an interactive system for reviewers is planned.