{"title":"基于Bert模型的穴位分类*","authors":"Xiong Zhong, Yangli Jia, Dekui Li, Xiangliang Zhang","doi":"10.4236/jdaip.2021.93008","DOIUrl":null,"url":null,"abstract":"In this \npaper, we explore the multi-classification problem of acupuncture acupoints based on Bert model, i.e., we try to recommend the \nbest main acupuncture point for treating the disease by classifying and \npredicting the main acupuncture point for the disease, and further explore its \nacupuncture point grouping to provide the medical practitioner with the optimal \nsolution for treating the disease and improving the \nclinical decision-making ability. The Bert-Chinese-Acupoint model was \nconstructed by retraining on the basis of the Bert model, and the semantic features in terms of acupuncture points were \nadded to the acupuncture point corpus in the fine-tuning process to \nincrease the semantic features in terms of acupuncture points, and compared with the machine learning method. The results \nshow that the Bert-Chinese Acupoint model proposed in this paper has a 3% \nimprovement in accuracy compared to the best \nperforming model in the machine learning approach.","PeriodicalId":71434,"journal":{"name":"数据分析和信息处理(英文)","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2021-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Classification of Acupuncture Points Based on the Bert Model*\",\"authors\":\"Xiong Zhong, Yangli Jia, Dekui Li, Xiangliang Zhang\",\"doi\":\"10.4236/jdaip.2021.93008\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this \\npaper, we explore the multi-classification problem of acupuncture acupoints based on Bert model, i.e., we try to recommend the \\nbest main acupuncture point for treating the disease by classifying and \\npredicting the main acupuncture point for the disease, and further explore its \\nacupuncture point grouping to provide the medical practitioner with the optimal \\nsolution for treating the disease and improving the \\nclinical decision-making ability. The Bert-Chinese-Acupoint model was \\nconstructed by retraining on the basis of the Bert model, and the semantic features in terms of acupuncture points were \\nadded to the acupuncture point corpus in the fine-tuning process to \\nincrease the semantic features in terms of acupuncture points, and compared with the machine learning method. The results \\nshow that the Bert-Chinese Acupoint model proposed in this paper has a 3% \\nimprovement in accuracy compared to the best \\nperforming model in the machine learning approach.\",\"PeriodicalId\":71434,\"journal\":{\"name\":\"数据分析和信息处理(英文)\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-07-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"数据分析和信息处理(英文)\",\"FirstCategoryId\":\"1093\",\"ListUrlMain\":\"https://doi.org/10.4236/jdaip.2021.93008\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"数据分析和信息处理(英文)","FirstCategoryId":"1093","ListUrlMain":"https://doi.org/10.4236/jdaip.2021.93008","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
摘要
本文探讨了基于Bert模型的针灸穴位多分类问题,即通过对疾病的主要穴位进行分类和预测,尝试推荐治疗疾病的最佳主穴位,并进一步探索其穴位分组,为医生提供治疗疾病的最优方案,提高临床决策能力。在Bert模型的基础上通过再训练构建Bert- chinese -腧穴模型,并在微调过程中将穴位方面的语义特征添加到穴位语料库中,增加穴位方面的语义特征,并与机器学习方法进行比较。结果表明,与机器学习方法中表现最好的模型相比,本文提出的Bert-Chinese穴位模型的准确率提高了3%。
Classification of Acupuncture Points Based on the Bert Model*
In this
paper, we explore the multi-classification problem of acupuncture acupoints based on Bert model, i.e., we try to recommend the
best main acupuncture point for treating the disease by classifying and
predicting the main acupuncture point for the disease, and further explore its
acupuncture point grouping to provide the medical practitioner with the optimal
solution for treating the disease and improving the
clinical decision-making ability. The Bert-Chinese-Acupoint model was
constructed by retraining on the basis of the Bert model, and the semantic features in terms of acupuncture points were
added to the acupuncture point corpus in the fine-tuning process to
increase the semantic features in terms of acupuncture points, and compared with the machine learning method. The results
show that the Bert-Chinese Acupoint model proposed in this paper has a 3%
improvement in accuracy compared to the best
performing model in the machine learning approach.