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Updated: Mar 19, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Topic detection using paragraph vectors to support active learning in systematic reviews.

Kazuma Hashimoto¹, Georgios Kontonatsios², Makoto Miwa³

¹Graduate School of Engineering, University of Tokyo, Tokyo, Japan.

Journal of Biomedical Informatics

|June 14, 2016

Summary

This summary is machine-generated.

This study introduces a new topic detection method using neural networks to improve citation screening for systematic reviews. The approach significantly reduces manual annotation costs while maintaining high accuracy in identifying relevant studies.

Keywords:

Active learning Citation screening Document embeddings Paragraph vectors Systematic reviews Topic modelling

Related Experiment Videos

Last Updated: Mar 19, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Area of Science:

Computational biology
Bioinformatics
Machine learning in healthcare

Background:

Systematic reviews require extensive manual screening of thousands of citations.
Active learning text classification semi-automates this process, reducing manual workload.
Existing methods may not fully capture the nuances of study representations for optimal classification.

Purpose of the Study:

To present a novel topic detection method for enhancing active learning in systematic reviews.
To improve the performance of active learners by inducing informative study representations.
To reduce the manual annotation cost in systematic reviews through semi-automation.

Main Methods:

Utilized a neural network-based vector space model to capture semantic document similarities.
Clustered documents into predefined clusters, with centroids representing latent topics.
Represented each document as a mixture of these latent topics for active learning.

Main Results:

The novel topic detection method achieved high sensitivity in identifying eligible studies.
Demonstrated a significantly reduced manual annotation cost compared to the Latent Dirichlet Allocation baseline.
Results were consistent across two clinical and three public health systematic reviews.

Conclusions:

The proposed neural network-based topic detection method effectively improves active learning for systematic reviews.
This approach offers a substantial reduction in manual screening effort and cost.
The method shows promise for accelerating evidence synthesis in clinical and public health research.