@Article{info:doi/10.2196/54803,
author="Li, Jiajia
and Wang, Zikai
and Yu, Longxuan
and Liu, Hui
and Song, Haitao",
title="Synthetic Data-Driven Approaches for Chinese Medical Abstract Sentence Classification: Computational Study",
journal="JMIR Form Res",
year="2025",
month="Mar",
day="19",
volume="9",
pages="e54803",
keywords="medical abstract sentence classification; large language models; synthetic datasets; deep learning; Chinese medical; dataset; traditional Chinese medicine; global medical research; algorithm; robustness; efficiency; accuracy",
abstract="Background: Medical abstract sentence classification is crucial for enhancing medical database searches, literature reviews, and generating new abstracts. However, Chinese medical abstract classification research is hindered by a lack of suitable datasets. Given the vastness of Chinese medical literature and the unique value of traditional Chinese medicine, precise classification of these abstracts is vital for advancing global medical research. Objective: This study aims to address the data scarcity issue by generating a large volume of labeled Chinese abstract sentences without manual annotation, thereby creating new training datasets. Additionally, we seek to develop more accurate text classification algorithms to improve the precision of Chinese medical abstract classification. Methods: We developed 3 training datasets (dataset {\#}1, dataset {\#}2, and dataset {\#}3) and a test dataset to evaluate our model. Dataset {\#}1 contains 15,000 abstract sentences translated from the PubMed dataset into Chinese. Datasets {\#}2 and {\#}3, each with 15,000 sentences, were generated using GPT-3.5 from 40,000 Chinese medical abstracts in the CSL database. Dataset {\#}2 used titles and keywords for pseudolabeling, while dataset {\#}3 aligned abstracts with category labels. The test dataset includes 87,000 sentences from 20,000 abstracts. We used SBERT embeddings for deeper semantic analysis and evaluated our model using clustering (SBERT-DocSCAN) and supervised methods (SBERT-MEC). Extensive ablation studies and feature analyses were conducted to validate the model's effectiveness and robustness. Results: Our experiments involved training both clustering and supervised models on the 3 datasets, followed by comprehensive evaluation using the test dataset. The outcomes demonstrated that our models outperformed the baseline metrics. Specifically, when trained on dataset {\#}1, the SBERT-DocSCAN model registered an impressive accuracy and F1-score of 89.85{\%} on the test dataset. Concurrently, the SBERT-MEC algorithm exhibited comparable performance with an accuracy of 89.38{\%} and an identical F1-score. Training on dataset {\#}2 yielded similarly positive results for the SBERT-DocSCAN model, achieving an accuracy and F1-score of 89.83{\%}, while the SBERT-MEC algorithm recorded an accuracy of 86.73{\%} and an F1-score of 86.51{\%}. Notably, training with dataset {\#}3 allowed the SBERT-DocSCAN model to attain the best with an accuracy and F1-score of 91.30{\%}, whereas the SBERT-MEC algorithm also showed robust performance, obtaining an accuracy of 90.39{\%} and an F1-score of 90.35{\%}. Ablation analysis highlighted the critical role of integrated features and methodologies in improving classification efficiency. Conclusions: Our approach addresses the challenge of limited datasets for Chinese medical abstract classification by generating novel datasets. The deployment of SBERT-DocSCAN and SBERT-MEC models significantly enhances the precision of classifying Chinese medical abstracts, even when using synthetic datasets with pseudolabels. ",
issn="2561-326X",
doi="10.2196/54803",
url="https://formative.jmir.org/2025/1/e54803",
url="https://doi.org/10.2196/54803"
}