Improving Systematic Review Updates With Natural Language Processing Through Abstract Component Classification and Selection: Algorithm Development and Validation
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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.
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.