MPAExpo-LM: Fine-Tuned Large Language Model for Mycophenolic Acid Exposure Estimation After Renal Transplantation

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 (C₂, C₄, C₈) and 7.43 mg·h/L with four points (C_0.5, C₂, C₆, C₈). 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.