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

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

Published on: February 23, 2019

Automated Category and Trend Analysis of Scientific Articles on Ophthalmology Using Large Language Models:

Hina Raja¹, Asim Munawar², Nikolaos Mylonas³

¹Department of Ophthalmology, University of Tennessee Health Science Center, Memphis, TN, United States.

JMIR Formative Research

|March 22, 2024

Summary

This study introduces an automated method for classifying ophthalmology research using large language models (LLMs). The developed system achieved high accuracy, improving efficiency in scientific literature review and trend analysis.

Keywords:

BART BERT Bidirectional and Auto-Regressive Transformers LLM bidirectional encoder representations from transformers large language model ophthalmology text classification trend analysis

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Area of Science:

Ophthalmology
Artificial Intelligence
Natural Language Processing

Background:

Automated article classification is crucial for managing scientific literature.
Large Language Models (LLMs) offer advanced capabilities for text analysis.

Purpose of the Study:

To evaluate the effectiveness of various LLMs for classifying scientific ophthalmology papers.
To assess LLM performance on a curated dataset of ocular disease articles.

Main Methods:

Developed an NLP model utilizing zero-shot learning LLMs.
Compared Bidirectional and Auto-Regressive Transformers (BART) variants with BERT variants (distilBERT, SciBERT, PubmedBERT, BioBERT).
Used a dataset of 1000 annotated ocular disease articles (RenD dataset) for evaluation.

Main Results:

LLMs effectively categorized ophthalmology papers with high accuracy.
Achieved a mean accuracy of 0.86 and a mean F1-score of 0.85 on the RenD dataset.
Demonstrated efficiency gains by reducing human intervention in classification.

Conclusions:

The proposed LLM framework significantly improves accuracy and efficiency in scientific article classification.
Facilitates knowledge organization, retrieval, and trend analysis in ophthalmology.
The model's extendibility offers broad applications for research across diverse scientific fields.