MPAExpo-LM: Fine-Tuned Large Language Model for Mycophenolic Acid Exposure Estimation After Renal Transplantation.
basic_science · Level V
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- Record sourced from PubMed, PMID 41911138.
- Also identified by DOI 10.1109/JBHI.2026.3678339.
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Abstract
An accurate estimation of mycophenolic acid (MPA) exposure after renal transplantation is important for reducing acute rejection in patients treated with mycophenolate mofetil (MMF) or enteric-coated mycophenolate sodium (MPS). We propose MPAExpo-LM, a fine-tuned large language model with a differential attention denoising module for this task. Experiments on real-world datasets demonstrate that MPAExpo-LM achieves superior results compared to existing methods in the task, offering a new pathway for precise immunosuppressive therapy. It attains an RMSE of 8.78 mg·h/L using three sampling points (C<sub>2</sub>, C<sub>4</sub>, C<sub>8</sub>) and 7.43 mg·h/L with four points (C<sub>0.5</sub>, C<sub>2</sub>, C<sub>6</sub>, C<sub>8</sub>). Furthermore, we conducted additional fine-tuning by incorporating a limited amount of data from external data source from another hospital. In the subsequent multi-center validation, the model demonstrated robust generalization, achieving RMSEs of 7.342 mg·h/L (three points) and 7.281 mg·h/L (four points). Via reinforcement fine-tuning, the model demonstrates strong generalization capability in cross-hospital tests.