摘要
目的研究一种自动提取针灸临床文献中针灸处方的方法,辅助针灸处方数据挖掘,支持针灸治疗的临床研究及决策。方法检索1992年1月1日-2022年12月31日中国期刊全文数据库(CNKI)中针灸临床试验中文期刊文献,随机选取750篇文献的题目及摘要进行人工标注,选择针灸处方的疾病名称、刺灸法和穴位3类主要实体,从数据集中选取70%为训练集,15%为验证集,15%为测试集进行试验。将针灸处方提取视为一种序列标注任务,使用预训练语言模型(PLM)构建自动提取针灸处方的模型,并选取4种不同PLM,比较其实体识别效果,进一步研究负采样和标签平滑训练技术对模型的影响。结果基于eHealth的模型F1值最大(92.84)。训练时,若仅采用负采样技术,F1值增大至93.53;只采用标签平滑技术,F1值增至93.64;同时采用负采样和标签平滑技术,F1值增至94.28,提高了1.55%。结论基于生物医学领域eHealth的模型提取针灸处方的识别效果最佳,同时采用负采样和标签平滑训练技术可进一步提高模型识别效果。
s of 750 articles were randomly selected and manually labeled.The three main entities of disease name,acupuncture and moxibustion method and acupoint of acupuncture and moxibustion prescription were selected.From the data set,70%was selected as the training set,15%as the validation set,and 15%as the test set for the experiment.The extraction of acupuncture prescriptions was considered a sequence labeling task.A model for automatic extraction of acupuncture prescriptions was built using a pretrained language model(PLM),and four different PLMs were selected to compare their entity recognition effects.The impact of negative sampling and label smoothing training techniques on the model was further investigated.Results The model based on eHealth had the highest F1 scores(92.84).During training,if only negative sampling technology was used,F1 value increased to 93.53;if only label smoothing was used,F1 value increased to 93.64;if negative sampling and label smoothing were used simultaneously,F1 value increased to 94.28,an increase of 1.55%.Conclusions This study proposes a fast and accurate model for extracting acupuncture and moxibustion prescriptions.The research shows that the model recognition effect based on eHealth in the biomedical field is the best,and the recognition effect of the model can be further improved by using negative sampling and label smoothing training techniques.
作者
李英
姜岳波
关玲
Li Ying;Jiang Yuebo;Guan Ling(Department of Traditional Chinese Medicine(Acupuncture and Moxibustion),the First Medical Center of Chinese PLA General Hospital,Beijing 100853,China;Department of Acupuncture and Moxibustion,the Sixth Medical Center of Chinese PLA General Hospital,Beijing 100048,China)
出处
《国际中医中药杂志》
2024年第11期1506-1510,共5页
International Journal of Traditional Chinese Medicine
基金
国家自然科学基金(82174480)
解放军总医院青年自主创新科学基金项目(22QNFC125)。
关键词
针灸处方
自然语言处理
预训练语言模型
Acupuncture and moxibustion prescription
Natural language processing
Pre-trained language model
