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Updated: Jun 19, 2026

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

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Improving Systematic Review Updates With Natural Language Processing Through Abstract Component Classification and

Tatsuki Hasegawa¹, Hayato Kizaki¹, Keisho Ikegami¹

¹Division of Drug Informatics, Keio University Faculty of Pharmacy, Tokyo, Japan.

JMIR Medical Informatics

|March 27, 2025

Summary

This summary is machine-generated.

Selective training on abstract components enhances systematic review screening models, significantly reducing manual workload. This approach improves article screening efficiency for systematic review updates.

Keywords:

bidirectional encoder representations from transformer efficiency guideline updates language model literature natural language processing screening model systematic review updating systematic reviews

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Area of Science:

Bibliometrics
Information Science
Computational Linguistics

Background:

Systematic review updates face challenges due to extensive article screening workloads.
Current natural language processing (NLP) screening models often treat abstracts uniformly, limiting performance.
Selective training on specific abstract components is hypothesized to improve model efficacy.

Purpose of the Study:

To evaluate a novel screening model that utilizes specific abstract components for improved performance.
To develop an automated systematic review update model employing an abstract component classifier.

Main Methods:

Developed screening models using component-composition datasets derived from manually classified abstract components (Title, Introduction, Methods, Results, Conclusion).
Compared performance of models using Bidirectional Encoder Representations from Transformer (BERT), BioLinkBERT, and BioM-ELECTRA pre-trained models.
Created an Abstract Component Classifier Model to automate component selection and developed models using these automatically classified datasets.

Main Results:

Some models trained on specific components outperformed those trained on entire abstracts across all tested pre-trained models.
Models using automatically classified components also surpassed full-abstract models in performance.
Achieved an 88.6% reduction in manual screening workload with high recall (0.93).

Conclusions:

Component selection from titles and abstracts demonstrably enhances screening model performance.
This method substantially reduces manual screening workload for systematic review updates.
Further validation across diverse systematic review domains is recommended.