目的针对中医个性化处方推荐问题,研究自动化处方推荐任务,为中医临床辅助决策提供参考。方法基于大语言预训练文本生成模型设计一种中医个性化处方推荐算法。将中医处方推荐任务转化为端到端(seq2seq)的文本生成任务,即将临床症状描述...目的针对中医个性化处方推荐问题,研究自动化处方推荐任务,为中医临床辅助决策提供参考。方法基于大语言预训练文本生成模型设计一种中医个性化处方推荐算法。将中医处方推荐任务转化为端到端(seq2seq)的文本生成任务,即将临床症状描述文本通过模型转化为处方文本,以实现处方推荐任务的需求,并利用基于大语言预训练的BART(Bidirectional and Auto-Regressive Transformers)模型的预训练参数来提升模型对通用语义信息的理解,通过对训练集处方内中药排序提升模型的处方推荐性能。结果实验证明通过大语言预训练模型以及端到端的文本生成架构可有效提升模型的生成性能,同时对处方内中药依次排序可以获取更高准确率,并且通过中药的排列获取更多值得参考的有价值信息。中医个性化处方推荐模型在处方排序后分别在前5、10、15味生成的处方分别取得了58.60、53.79和49.67的准确率。结论中医个性化处方推荐模型取得了更优的处方推荐效果,表明其可为中医临床治疗疾病进行参考,达到辅助临床决策支持的效果。展开更多
中医诊疗在我国历史悠久,但是从计算机角度对中医症状与中药关系的研究很少。本研究基于中医诊疗规则,运用卷积神经网络技术,实现从中医病症到中草药的自动推荐(CNN-based Herb Prescription,CNN-HP)。具体地,本研究提出一种含单个卷积...中医诊疗在我国历史悠久,但是从计算机角度对中医症状与中药关系的研究很少。本研究基于中医诊疗规则,运用卷积神经网络技术,实现从中医病症到中草药的自动推荐(CNN-based Herb Prescription,CNN-HP)。具体地,本研究提出一种含单个卷积层和三个全连接层的卷积神经网络模型,并与现有中草药推荐算法以及经典的机器学习算法进行了推荐性能的比较。定量和定性的实验结果表明,CNN-HP模型的推荐性能高于对比算法,其精确度为71.54%,召回率为87.09%,F1分数为78.55%。本研究为中医病症到中草药的自动推荐提供了新思路。展开更多
Objective To develop and evaluate a fine-tuned large language model(LLM)for traditional Chinese medicine(TCM)prescription recommendation named TCMLLM-PR.Methods First,we constructed an instruction-tuning dataset conta...Objective To develop and evaluate a fine-tuned large language model(LLM)for traditional Chinese medicine(TCM)prescription recommendation named TCMLLM-PR.Methods First,we constructed an instruction-tuning dataset containing 68654 samples(ap-proximately 10 million tokens)by integrating data from eight sources,including four TCM textbooks,Pharmacopoeia of the People’s Republic of China 2020(CHP),Chinese Medicine Clinical Cases(CMCC),and hospital clinical records covering lung disease,liver disease,stroke,diabetes,and splenic-stomach disease.Then,we trained TCMLLM-PR using Chat-GLM-6B with P-Tuning v2 technology.The evaluation consisted of three aspects:(i)compari-son with traditional prescription recommendation models(PTM,TCMPR,and PresRecST);(ii)comparison with TCM-specific LLMs(ShenNong,Huatuo,and HuatuoGPT)and general-domain ChatGPT;(iii)assessment of model migration capability across different disease datasets.We employed precision,recall,and F1 score as evaluation metrics.Results The experiments showed that TCMLLM-PR significantly outperformed baseline models on TCM textbooks and CHP datasets,with F1@10 improvements of 31.80%and 59.48%,respectively.In cross-dataset validation,the model performed best when migrating from TCM textbooks to liver disease dataset,achieving an F1@10 of 0.1551.Analysis of real-world cases demonstrated that TCMLLM-PR's prescription recommendations most closely matched actual doctors’prescriptions.Conclusion This study integrated LLMs into TCM prescription recommendations,leverag-ing a tailored instruction-tuning dataset and developing TCMLLM-PR.This study will pub-licly release the best model parameters of TCMLLM-PR to promote the development of the decision-making process in TCM practices(https://github.com/2020MEAI/TCMLLM).展开更多
文摘目的针对中医个性化处方推荐问题,研究自动化处方推荐任务,为中医临床辅助决策提供参考。方法基于大语言预训练文本生成模型设计一种中医个性化处方推荐算法。将中医处方推荐任务转化为端到端(seq2seq)的文本生成任务,即将临床症状描述文本通过模型转化为处方文本,以实现处方推荐任务的需求,并利用基于大语言预训练的BART(Bidirectional and Auto-Regressive Transformers)模型的预训练参数来提升模型对通用语义信息的理解,通过对训练集处方内中药排序提升模型的处方推荐性能。结果实验证明通过大语言预训练模型以及端到端的文本生成架构可有效提升模型的生成性能,同时对处方内中药依次排序可以获取更高准确率,并且通过中药的排列获取更多值得参考的有价值信息。中医个性化处方推荐模型在处方排序后分别在前5、10、15味生成的处方分别取得了58.60、53.79和49.67的准确率。结论中医个性化处方推荐模型取得了更优的处方推荐效果,表明其可为中医临床治疗疾病进行参考,达到辅助临床决策支持的效果。
文摘中医诊疗在我国历史悠久,但是从计算机角度对中医症状与中药关系的研究很少。本研究基于中医诊疗规则,运用卷积神经网络技术,实现从中医病症到中草药的自动推荐(CNN-based Herb Prescription,CNN-HP)。具体地,本研究提出一种含单个卷积层和三个全连接层的卷积神经网络模型,并与现有中草药推荐算法以及经典的机器学习算法进行了推荐性能的比较。定量和定性的实验结果表明,CNN-HP模型的推荐性能高于对比算法,其精确度为71.54%,召回率为87.09%,F1分数为78.55%。本研究为中医病症到中草药的自动推荐提供了新思路。
基金National Key Research and Development Program(2023YFC3502604)National Natural Science Foundation of China(U23B2062 and 82374302).
文摘Objective To develop and evaluate a fine-tuned large language model(LLM)for traditional Chinese medicine(TCM)prescription recommendation named TCMLLM-PR.Methods First,we constructed an instruction-tuning dataset containing 68654 samples(ap-proximately 10 million tokens)by integrating data from eight sources,including four TCM textbooks,Pharmacopoeia of the People’s Republic of China 2020(CHP),Chinese Medicine Clinical Cases(CMCC),and hospital clinical records covering lung disease,liver disease,stroke,diabetes,and splenic-stomach disease.Then,we trained TCMLLM-PR using Chat-GLM-6B with P-Tuning v2 technology.The evaluation consisted of three aspects:(i)compari-son with traditional prescription recommendation models(PTM,TCMPR,and PresRecST);(ii)comparison with TCM-specific LLMs(ShenNong,Huatuo,and HuatuoGPT)and general-domain ChatGPT;(iii)assessment of model migration capability across different disease datasets.We employed precision,recall,and F1 score as evaluation metrics.Results The experiments showed that TCMLLM-PR significantly outperformed baseline models on TCM textbooks and CHP datasets,with F1@10 improvements of 31.80%and 59.48%,respectively.In cross-dataset validation,the model performed best when migrating from TCM textbooks to liver disease dataset,achieving an F1@10 of 0.1551.Analysis of real-world cases demonstrated that TCMLLM-PR's prescription recommendations most closely matched actual doctors’prescriptions.Conclusion This study integrated LLMs into TCM prescription recommendations,leverag-ing a tailored instruction-tuning dataset and developing TCMLLM-PR.This study will pub-licly release the best model parameters of TCMLLM-PR to promote the development of the decision-making process in TCM practices(https://github.com/2020MEAI/TCMLLM).
