Enhancing Clinical Text Classification via Fine-Tuned DRAGON Longformer Models

TL;DR

This paper improves clinical text classification by fine-tuning the DRAGON Longformer model with domain-specific adjustments, resulting in significantly better accuracy, precision, recall, and F1-score on medical case data.

Contribution

The study introduces specific enhancements to the DRAGON Longformer for clinical text, including increased sequence length and medical terminology integration, achieving substantial performance improvements.

Findings

Accuracy increased from 72.0% to 85.2%.

Precision improved from 68.0% to 84.1%.

Recall rose from 75.0% to 86.3%.

Abstract

This study explores the optimization of the DRAGON Longformer base model for clinical text classification, specifically targeting the binary classification of medical case descriptions. A dataset of 500 clinical cases containing structured medical observations was used, with 400 cases for training and 100 for validation. Enhancements to the pre-trained joeranbosma/dragon-longformer-base-mixed-domain model included hyperparameter tuning, domain-specific preprocessing, and architectural adjustments. Key modifications involved increasing sequence length from 512 to 1024 tokens, adjusting learning rates from 1e-05 to 5e-06, extending training epochs from 5 to 8, and incorporating specialized medical terminology. The optimized model achieved notable performance gains: accuracy improved from 72.0% to 85.2%, precision from 68.0% to 84.1%, recall from 75.0% to 86.3%, and F1-score from 71.0% to 85.2%. Statistical analysis confirmed the significance of these improvements (p < .001). The model demonstrated enhanced capability in interpreting medical terminology, anatomical measurements, and clinical observations. These findings contribute to domain-specific language model research and offer practical implications for clinical natural language processing applications. The optimized model's strong performance across diverse medical conditions underscores its potential for broad use in healthcare settings.