  Traditional Chinese medicine (TCM) relies on specific combinations of herbs
in prescriptions to treat symptoms and signs, a practice that spans thousands
of years. Predicting TCM prescriptions presents a fascinating technical
challenge with practical implications. However, this task faces limitations due
to the scarcity of high-quality clinical datasets and the intricate
relationship between symptoms and herbs. To address these issues, we introduce
DigestDS, a new dataset containing practical medical records from experienced
experts in digestive system diseases. We also propose a method, TCM-FTP (TCM
Fine-Tuning Pre-trained), to leverage pre-trained large language models (LLMs)
through supervised fine-tuning on DigestDS. Additionally, we enhance
computational efficiency using a low-rank adaptation technique. TCM-FTP also
incorporates data augmentation by permuting herbs within prescriptions,
capitalizing on their order-agnostic properties. Impressively, TCM-FTP achieves
an F1-score of 0.8031, surpassing previous methods significantly. Furthermore,
it demonstrates remarkable accuracy in dosage prediction, achieving a
normalized mean square error of 0.0604. In contrast, LLMs without fine-tuning
perform poorly. Although LLMs have shown capabilities on a wide range of tasks,
this work illustrates the importance of fine-tuning for TCM prescription
prediction, and we have proposed an effective way to do that.
