CHINESE JOURNAL OF MEDICINAL GUIDE >

2026 , Vol. 28 >Issue 6: 767 - 767-774

DOI: https://doi.org/10.1009-0959.2026.030014

Intelligent Classification of Interpretability for Clinical Trial Quality Issue Text Based on Large Language Models

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  • 1.Department of Biomedical Engineering， Southern University of Science and Technology， Guangdong Shenzhen 518055， China；
    2.The University of HongKong-Shenzhen Hospital Clinical Trials Center， Guangdong Shenzhen 518053， China；
    3.School of Artificial Intelligence in Medicine， Guilin Medical University， Guangxi Guilin 541199， China；
    4.College of Life Sciences and Oceanography， Shenzhen University， Guangdong Shenzhen 518060， China；
    5.Faculty of Pharmaceutical Sciences， Shenzhen University of Advanced Technology， Guangdong Shenzhen 518107， China

Received date: 2025-12-22

  Revised date: 2026-03-04

  Accepted date: 2026-03-18

  Online published: 2026-08-17

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Abstract

Objective： To investigate the capability of large language models （LLMs） in recognizing and classifying quality issue texts of clinical trials.Methods： A text classification framework comprising 12 primary and 67 secondary indicators was established. A total of 1035 quality issues texts were collected and augmented to 5733 samples using LLMs. Three models （Qwen3， Bert， Gemma3） were fine-tuned with LoRA and evaluated， with Shap employed for interpretability analysis.Results： Bert achieved the best performance， with post-augmentation accuracies of 90.3% for primary and 81.2% for secondary indicators-improvements of 11.8% and 36.8%， respectively. Although Bert outperformed overall， Qwen3 and Gemma3 exceeded Bert by 0.4%-13.4% in specific scenarios such as biospecimen collection， laboratory tests， and concomitant medication. Shap analysis confirmed alignment between model decisions and human reasoning.Conclusion： Fine-tuned LLMs can effectively classify clinical trials quality issue texts， offering a viable solution to address inconsistencies arising from varying practitioner expertise and judgment standards， thereby supporting manual quality control workflows.


Key words： Large language models； ; Clinical trials； ; Quality issues； ; Quality control； ; Classification； ; Interpretability analysis

Cite this article

XU Lei, YU Man, ZHUO Baoshan, XU Yanping, HUANG Longkai, LUO Jiahui, KONG Yi, ZHOU Wenjing .

Intelligent Classification of Interpretability for Clinical Trial Quality Issue Text Based on Large Language Models

[J]. CHINESE JOURNAL OF MEDICINAL GUIDE, 2026 , 28(6) : 767 -767-774 . DOI: 10.1009-0959.2026.030014

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