Objective This study aimed to examine and propagate the medication experience and group formula of traditional Chinese medicine(TCM)Master XIONG Jibo in diagnosing and treat-ing arthralgia syndrome(AS)through data min...Objective This study aimed to examine and propagate the medication experience and group formula of traditional Chinese medicine(TCM)Master XIONG Jibo in diagnosing and treat-ing arthralgia syndrome(AS)through data mining.Methods Data of outpatient cases of Professor XIONG Jibo were collected from January 1,2014 to December 31,2018,along with cases recorded in A Real Famous Traditional Chinese Medicine Doctor:XIONG Jibo's Clinical Medical Record 1,which was published in December 2019.The five variables collected from the patients’data were TCM diagnostic information,TCM and western medicine diagnoses,syndrome,treatment,and prescription.A database was established for the collected data with Excel.Using the Python environment,a custom-ized modified natural language processing(NLP)model for the diagnosis and treatment of AS by Professor XIONG Jibo was established to preprocess the data and to analyze the word cloud.Frequency analysis,association rule analysis,cluster analysis,and visual analysis of AS cases were performed based on the Traditional Chinese Medicine Inheritance Computing Platform(V3.0)and RStudio(V4.0.3).Results A total of 610 medical records of Professor XIONG Jibo were collected from the case database.A total of 103 medical records were included after data screening criteria,which comprised 187 times(45 kinds)of prescriptions and 1506 times(125 kinds)of Chinese herbs.The main related meridians were the liver,spleen,and kidney meridians.The properties of Chinese herbs used most were mainly warm,flat,and cold,while the flavors of herbs were mainly bitter,pungent,and sweet.The main patterns of AS included the damp heat,phlegm stasis,and neck arthralgia.The most commonly used herbs for AS were Chuanniuxi(Cyathu-lae Radix),Huangbo(Phellodendri Chinensis Cortex),Cangzhu(Atractylodis Rhizoma),Qinjiao(Gentianae Macrophyllae Radix),Gancao(Glycyrrhizae Radix et Rhizoma),Huangqi(Astragali Radix),and Chuanxiong(Chuanxiong Rhizoma).The most common effect of the herbs was“promoting blood circulation and removing blood stasis”,followed by“supple-menting deficiency(Qi supplementing,blood supplementing,and Yang supplementing)”,and“dispelling wind and dampness”.The data were analyzed with the support≥15%and con-fidence=100%,and after de-duplication,five second-order association rules,39 third-order association rules,39 fourth-order association rules,and two fifth-order association rules were identified.The top-ranking association rules of each were“Cangzhu(Atractylodis Rhizoma)→Huangbo(Phellodendri Chinensis Cortex)”“Cangzhu(Atractylodis Rhizoma)+Chuanniuxi(Cyathulae Radix)→Huangbo(Phellodendri Chinensis Cortex)”“Chuanniuxi(Cyathulae Radix)+Danggui(Angelicae Sinensis Radix)+Gancao(Glycyrrhizae Radix et Rhizoma)→Qinjiao(Gentianae Macrophyllae Radix)”and“Chuanniuxi(Cyathulae Radix)+Danggui(Angelicae Sinensis Radix)+Gancao(Glycyrrhizae Radix et Rhizoma)+Huangbo(Phello-dendri Chinensis Cortex)→Qinjiao(Gentianae Macrophyllae Radix)”,respectively.Five clusters were obtained using cluster analysis of the top 30 herbs.The herbs were mainly dry-ing dampness,supplementing Qi,and promoting blood circulation.The main prescriptions of AS were Ermiao San(二妙散),Gegen Jianghuang San(葛根姜黄散),and Huangqi Chongteng Yin(黄芪虫藤饮).The herbs of core prescription included Cangzhu(Atractylodis Rhizoma),Chuanniuxi(Cyathulae Radix),Gancao(Glycyrrhizae Radix et Rhizoma),Huangbo(Phellodendri Chinensis Cortex),Mugua(Chaenomelis Fructus),Qinjiao(Gentianae Macro-phyllae Radix),Danggui(Angelicae Sinensis Radix),and Yiyiren(Coicis Semen).Conclusion Clearing heat and dampness,relieving collaterals and pain,and invigorating Qi and blood are the most commonly used therapies for the treatment of AS by Professor XIONG Jibo.Additionally,customized NLP model could improve the efficiency of data mining in TCM.展开更多
梳理总结现阶段BERT模型应用于医学中的研究热点和未来发展趋势,为我国医学信息化提供参考和建议。采用文献计量学方法,收集整理Web of Science数据库核心集(WoSCC)中从2018年1月1日至2022年12月31日医学应用BERT模型的相关文献并进行...梳理总结现阶段BERT模型应用于医学中的研究热点和未来发展趋势,为我国医学信息化提供参考和建议。采用文献计量学方法,收集整理Web of Science数据库核心集(WoSCC)中从2018年1月1日至2022年12月31日医学应用BERT模型的相关文献并进行分析。经筛选共纳入267篇文献。研究显示BERT主要应用在西医领域;参研国家主要为中国和美国,其他国家涉猎较少;作者单位分布呈现以高校为主,医疗机构及科研院所、政府机关等为辅的特征;研究内容主要聚焦于医疗信息抽取、命名实体识别等。中医领域应用BERT模型较早,但目前尚处于起步阶段,而我国健康卫生保障体系中西医并重,未来研究可围绕BERT如何促进中医信息化方面进一步扩展。展开更多
目的对中药治疗肾性贫血的方剂进行数据挖掘,探究中药治疗肾性贫血的用药规律。方法通过检索PubMed、the Cochrane Library、Web of Science、中国知网、万方、维普和中国生物医学文献数据库等中英文数据库,筛选中药治疗肾性贫血的相关...目的对中药治疗肾性贫血的方剂进行数据挖掘,探究中药治疗肾性贫血的用药规律。方法通过检索PubMed、the Cochrane Library、Web of Science、中国知网、万方、维普和中国生物医学文献数据库等中英文数据库,筛选中药治疗肾性贫血的相关文献。采用Excel整理并统计所有方药的信息,包括单味中药使用频次、性味、归经、功效;利用R语言对各味中药进行关联规则分析和层次聚类分析。结果共纳入文献268篇,涉及中药169味,总使用频次3919次,其中使用频次≥100次的药物有黄芪、当归、白术、大黄、茯苓、熟地黄、党参、丹参、川芎,药物性味以温药、甘味为主,归经以脾、肝、肾为主,功效以补气、养血、活血化瘀、利水渗湿、攻下为主。关联规则分析结果显示,核心组方为黄芪、当归、白术、茯苓、党参。层次聚类分析结果显示,聚类结果划分为1、2、3层,其中黄芪和当归簇集分类始终相同。结论肾性贫血治疗用核心组方为黄芪、当归、白术、茯苓、党参,其中黄芪和当归在核心组方中处于中心位置,不可或缺;用药规律主要以补气、养血、健脾和化湿等为主,可根据患者的不同症状特点辨证论治,在核心组方的基础上加减其他中药。展开更多
基金Project of State Administration of Traditional Chinese Medicine(GZY-YZS-2019-45)The Horizontal Project of Hunan Medical College(HYH-2021Y-KJ-6-33)+1 种基金Scientific Research Project of Hunan Provincial Department of Education in 2021(21C0223)Natural Science Foundation of Hunan Province in 2022(1524)。
文摘Objective This study aimed to examine and propagate the medication experience and group formula of traditional Chinese medicine(TCM)Master XIONG Jibo in diagnosing and treat-ing arthralgia syndrome(AS)through data mining.Methods Data of outpatient cases of Professor XIONG Jibo were collected from January 1,2014 to December 31,2018,along with cases recorded in A Real Famous Traditional Chinese Medicine Doctor:XIONG Jibo's Clinical Medical Record 1,which was published in December 2019.The five variables collected from the patients’data were TCM diagnostic information,TCM and western medicine diagnoses,syndrome,treatment,and prescription.A database was established for the collected data with Excel.Using the Python environment,a custom-ized modified natural language processing(NLP)model for the diagnosis and treatment of AS by Professor XIONG Jibo was established to preprocess the data and to analyze the word cloud.Frequency analysis,association rule analysis,cluster analysis,and visual analysis of AS cases were performed based on the Traditional Chinese Medicine Inheritance Computing Platform(V3.0)and RStudio(V4.0.3).Results A total of 610 medical records of Professor XIONG Jibo were collected from the case database.A total of 103 medical records were included after data screening criteria,which comprised 187 times(45 kinds)of prescriptions and 1506 times(125 kinds)of Chinese herbs.The main related meridians were the liver,spleen,and kidney meridians.The properties of Chinese herbs used most were mainly warm,flat,and cold,while the flavors of herbs were mainly bitter,pungent,and sweet.The main patterns of AS included the damp heat,phlegm stasis,and neck arthralgia.The most commonly used herbs for AS were Chuanniuxi(Cyathu-lae Radix),Huangbo(Phellodendri Chinensis Cortex),Cangzhu(Atractylodis Rhizoma),Qinjiao(Gentianae Macrophyllae Radix),Gancao(Glycyrrhizae Radix et Rhizoma),Huangqi(Astragali Radix),and Chuanxiong(Chuanxiong Rhizoma).The most common effect of the herbs was“promoting blood circulation and removing blood stasis”,followed by“supple-menting deficiency(Qi supplementing,blood supplementing,and Yang supplementing)”,and“dispelling wind and dampness”.The data were analyzed with the support≥15%and con-fidence=100%,and after de-duplication,five second-order association rules,39 third-order association rules,39 fourth-order association rules,and two fifth-order association rules were identified.The top-ranking association rules of each were“Cangzhu(Atractylodis Rhizoma)→Huangbo(Phellodendri Chinensis Cortex)”“Cangzhu(Atractylodis Rhizoma)+Chuanniuxi(Cyathulae Radix)→Huangbo(Phellodendri Chinensis Cortex)”“Chuanniuxi(Cyathulae Radix)+Danggui(Angelicae Sinensis Radix)+Gancao(Glycyrrhizae Radix et Rhizoma)→Qinjiao(Gentianae Macrophyllae Radix)”and“Chuanniuxi(Cyathulae Radix)+Danggui(Angelicae Sinensis Radix)+Gancao(Glycyrrhizae Radix et Rhizoma)+Huangbo(Phello-dendri Chinensis Cortex)→Qinjiao(Gentianae Macrophyllae Radix)”,respectively.Five clusters were obtained using cluster analysis of the top 30 herbs.The herbs were mainly dry-ing dampness,supplementing Qi,and promoting blood circulation.The main prescriptions of AS were Ermiao San(二妙散),Gegen Jianghuang San(葛根姜黄散),and Huangqi Chongteng Yin(黄芪虫藤饮).The herbs of core prescription included Cangzhu(Atractylodis Rhizoma),Chuanniuxi(Cyathulae Radix),Gancao(Glycyrrhizae Radix et Rhizoma),Huangbo(Phellodendri Chinensis Cortex),Mugua(Chaenomelis Fructus),Qinjiao(Gentianae Macro-phyllae Radix),Danggui(Angelicae Sinensis Radix),and Yiyiren(Coicis Semen).Conclusion Clearing heat and dampness,relieving collaterals and pain,and invigorating Qi and blood are the most commonly used therapies for the treatment of AS by Professor XIONG Jibo.Additionally,customized NLP model could improve the efficiency of data mining in TCM.
文摘梳理总结现阶段BERT模型应用于医学中的研究热点和未来发展趋势,为我国医学信息化提供参考和建议。采用文献计量学方法,收集整理Web of Science数据库核心集(WoSCC)中从2018年1月1日至2022年12月31日医学应用BERT模型的相关文献并进行分析。经筛选共纳入267篇文献。研究显示BERT主要应用在西医领域;参研国家主要为中国和美国,其他国家涉猎较少;作者单位分布呈现以高校为主,医疗机构及科研院所、政府机关等为辅的特征;研究内容主要聚焦于医疗信息抽取、命名实体识别等。中医领域应用BERT模型较早,但目前尚处于起步阶段,而我国健康卫生保障体系中西医并重,未来研究可围绕BERT如何促进中医信息化方面进一步扩展。
文摘目的对中药治疗肾性贫血的方剂进行数据挖掘,探究中药治疗肾性贫血的用药规律。方法通过检索PubMed、the Cochrane Library、Web of Science、中国知网、万方、维普和中国生物医学文献数据库等中英文数据库,筛选中药治疗肾性贫血的相关文献。采用Excel整理并统计所有方药的信息,包括单味中药使用频次、性味、归经、功效;利用R语言对各味中药进行关联规则分析和层次聚类分析。结果共纳入文献268篇,涉及中药169味,总使用频次3919次,其中使用频次≥100次的药物有黄芪、当归、白术、大黄、茯苓、熟地黄、党参、丹参、川芎,药物性味以温药、甘味为主,归经以脾、肝、肾为主,功效以补气、养血、活血化瘀、利水渗湿、攻下为主。关联规则分析结果显示,核心组方为黄芪、当归、白术、茯苓、党参。层次聚类分析结果显示,聚类结果划分为1、2、3层,其中黄芪和当归簇集分类始终相同。结论肾性贫血治疗用核心组方为黄芪、当归、白术、茯苓、党参,其中黄芪和当归在核心组方中处于中心位置,不可或缺;用药规律主要以补气、养血、健脾和化湿等为主,可根据患者的不同症状特点辨证论治,在核心组方的基础上加减其他中药。
