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2026 , Vol. 70 >Issue 15: 91 - 103

DOI: https://doi.org/10.13266/j.issn.0252-3116.2026.15.007

RESEARCH PAPERS

5-dBTFM-LLM-Driven Interpretable Recognition of Biomedical Translation Stages

  • Zhang Yunqiu ,
  • Huang Qifei
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  • The Department of Medical Informatics, School of Public Health, Jilin University, Changchun 130021
Zhang Yunqiu, Professor, PhD, Doctoral Supervisor, E-mail:yunqiu@jlu.edu.cn; Huang Qifei, Doctoral Candidate.

Received date: 2025-05-12

  Revised date: 2025-08-07

  Online published: 2026-07-30

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Abstract

[Purpose/Significance] This study investigates and proposes an interpretable method for identifying the transformation stages of biomedical research that integrates multi-dimensional features with large language models (LLMs), aiming to enhance the accuracy and interpretability of identification, thereby supporting the transformation evaluation and hierarchical assessment of biomedical research outcomes. [Method/Process] Firstly, a multi-dimensional transformation feature framework covering research objects, research technologies, research topics, research fields and clinical stages is constructed based on the basic-clinical-post-clinical transformation framework in the translational science spectrum. Then, the transformation features are extracted from the literature data based on the prompt learning strategy of the large language question-answering model, and the SBERT semantic embedding model is used to realize the vectorized representation of features. Finally, the quantized features are taken as input to construct a CatBoost-SHAP interpretable integrated learning model. Through feature importance analysis, the influence degree of each dimension feature on the transformation stage classification result is quantified, so as to realize the interpretability of stage identification. [Result/Conclusion] Empirical studies show that the AUC value identified by this method is significantly improved compared with the other two mainstream methods, achieving the optimal effect based on the same data set. The basic T0 stage is increased by 4.0% and 2.8% respectively; the clinical T1/T2 stage is increased by 9.1% and 4.7% respectively; the post-clinical T3/T4 stage is increased by 3.9% and 3.2% respectively. At the same time, the feature ablation experiment also proves the effectiveness of the five-dimensional transformation features in improving the method performance. In addition, the interpretability analysis also shows that the order of feature importance is: research topic, research field, research technology, research object and clinical stage. The study not only realizes multistage identification, but also improves the effect and interpretability of identification in terms of features and models. It has significant effectiveness and certain advantages, and can provide important method support for subsequent application scenarios such as scientific research management and policy formulation.

Key words： translational research; translational science spectrum; AIGC; ensemble learning; explainability

Cite this article

Zhang Yunqiu , Huang Qifei . 5-dBTFM-LLM-Driven Interpretable Recognition of Biomedical Translation Stages[J]. Library and Information Service, 2026 , 70(15) : 91 -103 . DOI: 10.13266/j.issn.0252-3116.2026.15.007

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