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
目的近年来,计算机辅助药物设计(computer aided drug design,CADD)发展迅速,受到了中外学者和医药界的广泛关注。系统了解CADD领域的发展进程,对科研人员和药物研发机构的研究方向和工作开展具有十分重要的指导意义和参考价值。方法以...目的近年来,计算机辅助药物设计(computer aided drug design,CADD)发展迅速,受到了中外学者和医药界的广泛关注。系统了解CADD领域的发展进程,对科研人员和药物研发机构的研究方向和工作开展具有十分重要的指导意义和参考价值。方法以中国知网(CNKI)和Web of Science(WOS)数据库作为数据来源,利用可视化工具CiteSpace软件,采用定性与定量相结合的研究方法总结归纳了2010—2022年区间段内发表的CADD文献,绘制科学知识图谱,从研究热点和演进趋势等方面展开分析。结果与结论研究结果显示,国内外关于CADD研究的侧重点各有不同,加快人工智能算法的实际应用,提高计算机药物设计的效率将成为新的研究方向。展开更多
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.
文摘目的近年来,计算机辅助药物设计(computer aided drug design,CADD)发展迅速,受到了中外学者和医药界的广泛关注。系统了解CADD领域的发展进程,对科研人员和药物研发机构的研究方向和工作开展具有十分重要的指导意义和参考价值。方法以中国知网(CNKI)和Web of Science(WOS)数据库作为数据来源,利用可视化工具CiteSpace软件,采用定性与定量相结合的研究方法总结归纳了2010—2022年区间段内发表的CADD文献,绘制科学知识图谱,从研究热点和演进趋势等方面展开分析。结果与结论研究结果显示,国内外关于CADD研究的侧重点各有不同,加快人工智能算法的实际应用,提高计算机药物设计的效率将成为新的研究方向。
