Harnessing Large Models, Distilling to Small: Localized Deployment for Accurate Medical Prescription Diagnostic Inference

Abstract

Diagnostic errors impose substantial healthcare costs. To address this, we propose BELL, a framework that leverages LLMs for data augmentation and distills knowledge into compact BERT models for efficient deployment. Our twostage framework first standardizes non-uniform clinical terms using fine-tuned BERT models, followed by multi-label disease prediction incorporating prescription data. Experiments on realworld anonymized data demonstrate BELL achieves 94.27 % standardization accuracy and improves diagnostic F1-score from 0.45 to 0.73, with 0.678 s average inference time.

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