1336
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2024-09-17
2024-09-17
近日，團(tuán)隊(duì)老師張連文、許玉龍參與，由香港科技大學(xué)、北京交通大學(xué)、中國(guó)中醫(yī)科學(xué)院、河南中醫(yī)藥大學(xué)合作的科研成果: 《TCM-FTP：中醫(yī)藥診斷大模型》，被IEEE International Conference on Bioinformatics and Biomedicine (BIBM2024) 會(huì)議錄用，IEEE BIBM會(huì)議是生物信息領(lǐng)域著名的會(huì)議，屬交叉/綜合/新興類(lèi)別 ，在CCF分級(jí)中為B類(lèi)會(huì)議，近三年的錄用率為19% 左右，在國(guó)際上有較高的影響力。
Abstract：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 lowrank adaptation technique. TCM-FTP also incorporates data augmentation by permuting herbs within prescriptions, capitalizing on their order-agnostic properties. Impressively, TCMFTP 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.
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