现代信息科技2024，Vol.8Issue(4)：153-161,166,10.DOI:10.19850/j.cnki.2096-4706.2024.04.032
两阶段多标签分类探索中医证素辨证规律
Exploration of the Rules of Traditional Chinese Medicine Syndrome Elements Differentiation by Two-stage Multi-label Classification
摘要
Abstract
Exploring the rules of syndrome element differentiation can better assist clinical decision-making and promote the inheritance of TCM syndrome differentiation theory.The sentence structure of TCM texts is complex,with varying expression standards,making it difficult to match symbol rules,and the black box characteristics of neural networks are difficult to directly explain their differentiation process.In order to explore the rules of syndrome element differentiation in TCM,it uses a neural network model to classify syndrome elements with multiple labels in the first stage,and generates syndrome element representation by capturing keywords and their weights related to syndrome elements through sparse attention.In the second stage,the random forest is used to conduct classified training on syndrome element representation incorporating relevant syndrome element labels,and then random forest rules are extracted to explore the syndrome differentiation rules and improve the interpretability of syndrome element differentiation.The experimental results show that this method improves the accuracy of syndrome element identification,while maintaining a high level of F1,which is conductive to exploring the rules of syndrome element differentiation.关键词
证素辨证规律/稀疏注意力/多标签分类/随机森林模型/可解释性Key words
rules of syndrome element differentiation/sparse attention/multi-label classification/random forest model/interpretability分类
信息技术与安全科学引用本文复制引用
蓝勇,程春雷,叶青,胡杭乐,沈友志..两阶段多标签分类探索中医证素辨证规律[J].现代信息科技,2024,8(4):153-161,166,10.基金项目
江西省自然科学基金资助项目(20224BAB206102) （20224BAB206102）
国家自然科学基金(82260988) （82260988）
江西省教育厅科学技术研究项目(GJJ2200923) （GJJ2200923）
江西省卫生和计划生育委员会-科技计划项目(202211404) （202211404）
江西中医药大学博士启动基金(2018WBZR021) （2018WBZR021）
