在循证医学时代下,依托规范的技术方法和标准化的操作规程发掘中医药独特优势,是实现中医药现代化、国际化发展并惠泽人类的必由之路。中医理论、人用经验和研究证据三结合证据体系的提出标志着中医药特色评价体系思维方法取得了重要进...在循证医学时代下,依托规范的技术方法和标准化的操作规程发掘中医药独特优势,是实现中医药现代化、国际化发展并惠泽人类的必由之路。中医理论、人用经验和研究证据三结合证据体系的提出标志着中医药特色评价体系思维方法取得了重要进步,经过恰当方法整合后的多元证据体是中医药临床指南推荐意见和循证卫生决策的有力支撑。本文基于当前国际证据合成与分级方法学前沿进展,初步提出中医药多元证据整合的方法学框架——MERGE(Merge Evidence-based Research and artificial intelliGence to support smart dEcision)框架,以期为中医药循证医学方法学体系的完善和发展提供借鉴和参考。展开更多
Objective To construct a precise model for identifying traditional Chinese medicine(TCM)constitutions;thereby offering optimized guidance for clinical diagnosis and treatment plan-ning;and ultimately enhancing medical...Objective To construct a precise model for identifying traditional Chinese medicine(TCM)constitutions;thereby offering optimized guidance for clinical diagnosis and treatment plan-ning;and ultimately enhancing medical efficiency and treatment outcomes.Methods First;TCM full-body inspection data acquisition equipment was employed to col-lect full-body standing images of healthy people;from which the constitutions were labelled and defined in accordance with the Constitution in Chinese Medicine Questionnaire(CCMQ);and a dataset encompassing labelled constitutions was constructed.Second;heat-suppres-sion valve(HSV)color space and improved local binary patterns(LBP)algorithm were lever-aged for the extraction of features such as facial complexion and body shape.In addition;a dual-branch deep network was employed to collect deep features from the full-body standing images.Last;the random forest(RF)algorithm was utilized to learn the extracted multifea-tures;which were subsequently employed to establish a TCM constitution identification mod-el.Accuracy;precision;and F1 score were the three measures selected to assess the perfor-mance of the model.Results It was found that the accuracy;precision;and F1 score of the proposed model based on multifeatures for identifying TCM constitutions were 0.842;0.868;and 0.790;respectively.In comparison with the identification models that encompass a single feature;either a single facial complexion feature;a body shape feature;or deep features;the accuracy of the model that incorporating all the aforementioned features was elevated by 0.105;0.105;and 0.079;the precision increased by 0.164;0.164;and 0.211;and the F1 score rose by 0.071;0.071;and 0.084;respectively.Conclusion The research findings affirmed the viability of the proposed model;which incor-porated multifeatures;including the facial complexion feature;the body shape feature;and the deep feature.In addition;by employing the proposed model;the objectification and intel-ligence of identifying constitutions in TCM practices could be optimized.展开更多
文摘在循证医学时代下,依托规范的技术方法和标准化的操作规程发掘中医药独特优势,是实现中医药现代化、国际化发展并惠泽人类的必由之路。中医理论、人用经验和研究证据三结合证据体系的提出标志着中医药特色评价体系思维方法取得了重要进步,经过恰当方法整合后的多元证据体是中医药临床指南推荐意见和循证卫生决策的有力支撑。本文基于当前国际证据合成与分级方法学前沿进展,初步提出中医药多元证据整合的方法学框架——MERGE(Merge Evidence-based Research and artificial intelliGence to support smart dEcision)框架,以期为中医药循证医学方法学体系的完善和发展提供借鉴和参考。
基金National Key Research and Development Program of China(2022YFC3502302)National Natural Science Foundation of China(82074580)Graduate Research Innovation Program of Jiangsu Province(KYCX23_2078).
文摘Objective To construct a precise model for identifying traditional Chinese medicine(TCM)constitutions;thereby offering optimized guidance for clinical diagnosis and treatment plan-ning;and ultimately enhancing medical efficiency and treatment outcomes.Methods First;TCM full-body inspection data acquisition equipment was employed to col-lect full-body standing images of healthy people;from which the constitutions were labelled and defined in accordance with the Constitution in Chinese Medicine Questionnaire(CCMQ);and a dataset encompassing labelled constitutions was constructed.Second;heat-suppres-sion valve(HSV)color space and improved local binary patterns(LBP)algorithm were lever-aged for the extraction of features such as facial complexion and body shape.In addition;a dual-branch deep network was employed to collect deep features from the full-body standing images.Last;the random forest(RF)algorithm was utilized to learn the extracted multifea-tures;which were subsequently employed to establish a TCM constitution identification mod-el.Accuracy;precision;and F1 score were the three measures selected to assess the perfor-mance of the model.Results It was found that the accuracy;precision;and F1 score of the proposed model based on multifeatures for identifying TCM constitutions were 0.842;0.868;and 0.790;respectively.In comparison with the identification models that encompass a single feature;either a single facial complexion feature;a body shape feature;or deep features;the accuracy of the model that incorporating all the aforementioned features was elevated by 0.105;0.105;and 0.079;the precision increased by 0.164;0.164;and 0.211;and the F1 score rose by 0.071;0.071;and 0.084;respectively.Conclusion The research findings affirmed the viability of the proposed model;which incor-porated multifeatures;including the facial complexion feature;the body shape feature;and the deep feature.In addition;by employing the proposed model;the objectification and intel-ligence of identifying constitutions in TCM practices could be optimized.
