Sleep disturbances and predictors of nondeployability among active-duty army soldiers: an odds ratio analysis of medical healthcare data from fiscal year 2018

Background:The impact of sleep disorders on active-duty soldiers’medical readiness is not currently quantified.Patient data generated at military treatment facilities can be accessed to create research reports and thus can be used to estimate the prevalence of sleep disturbances and the role of sleep on overall health in service members.The current study aimed to quantify sleep-related health issues and their impact on health and nondeployability through the analysis of U.S.military healthcare records from fiscal year 2018(FY2018).Methods:Medical diagnosis information and deployability profiles(e-Profiles)were queried for all active-duty U.S.Army patients with a concurrent sleep disorder diagnosis receiving medical care within FY2018.Nondeployability was predicted from medical reasons for having an e-Profile(categorized as sleep,behavioral health,musculoskeletal,cardiometabolic,injury,or accident)using binomial logistic regression.Sleep e-Profiles were investigated as a moderator between other e-Profile categories and nondeployability.Results:Out of 582,031 soldiers,48.4%(n=281,738)had a sleep-related diagnosis in their healthcare records,9.7%(n=56,247)of soldiers had e-Profiles,and 1.9%(n=10,885)had a sleep e-Profile.Soldiers with sleep e-Profiles were more likely to have had a motor vehicle accident(p OR(prevalence odds ratio)=4.7,95%CI 2.63–8.39,P≤0.001)or work/duty-related injury(p OR=1.6,95%CI 1.32–1.94,P≤0.001).The likelihood of nondeployability was greater in soldiers with a sleep e-Profile and a musculoskeletal e-Profile(p OR=4.25,95%CI 3.75–4.81,P≤0.001)or work/dutyrelated injury(p OR=2.62,95%CI 1.63–4.21,P≤0.001).Conclusion:Nearly half of soldiers had a sleep disorder or sleep-related medical diagnosis in 2018,but their sleep problems are largely not profiled as limitations to medical readiness.Musculoskeletal issues and physical injury predict nondeployability,and nondeployability is more likely to occur in soldiers who have sleep e-Profiles in addition to these issues.Addressing sleep problems may prevent accidents and injuries that could render a soldier nondeployable.

Translation in Data Mining to Advance Personalized Medicine for Health Equity

Personalized medicine is the development of “tailored” therapies that reflect traditional medical approaches with the incorporation of the patient’s unique genetic profile and the environmental basis of the disease. These individualized strategies encompass disease prevention and diagnosis, as well as treatment strategies. Today’s healthcare workforce is faced with the availability of massive amounts of patient- and disease-related data. When mined effectively, these data will help produce more efficient and effective diagnoses and treatment, leading to better prognoses for patients at both the individual and population level. Designing preventive and therapeutic interventions for those patients who will benefit most while minimizing side effects and controlling healthcare costs requires bringing diverse data sources together in an analytic paradigm. A resource to clinicians in the development and application of personalized medicine is largely facilitated, perhaps even driven, by the analysis of “big data”. For example, the availability of clinical data warehouses is a significant resource for clinicians in practicing personalized medicine. These “big data” repositories can be queried by clinicians, using specific questions, with data used to gain an understanding of challenges in patient care and treatment. Health informaticians are critical partners to data analytics including the use of technological infrastructures and predictive data mining strategies to access data from multiple sources, assisting clinicians’ interpretation of data and development of personalized, targeted therapy recommendations. In this paper, we look at the concept of personalized medicine, offering perspectives in four important, influencing topics: 1) the availability of “big data” and the role of biomedical informatics in personalized medicine, 2) the need for interdisciplinary teams in the development and evaluation of personalized therapeutic approaches, and 3) the impact of electronic medical record systems and clinical data warehouses on the field of personalized medicine. In closing, we present our fourth perspective, an overview to some of the ethical concerns related to personalized medicine and health equity.

Multi Attribute Case Based Privacy-preserving for Healthcare Transactional Data Using Cryptography

Medical data mining has become an essential task in healthcare sector to secure the personal and medical data of patients using privacy policy.In this background,several authentication and accessibility issues emerge with an inten-tion to protect the sensitive details of the patients over getting published in open domain.To solve this problem,Multi Attribute Case based Privacy Preservation(MACPP)technique is proposed in this study to enhance the security of privacy-preserving data.Private information can be any attribute information which is categorized as sensitive logs in a patient’s records.The semantic relation between transactional patient records and access rights is estimated based on the mean average value to distinguish sensitive and non-sensitive information.In addition to this,crypto hidden policy is also applied here to encrypt the sensitive data through symmetric standard key log verification that protects the personalized sensitive information.Further,linear integrity verification provides authentication rights to verify the data,improves the performance of privacy preserving techni-que against intruders and assures high security in healthcare setting.

基于数据挖掘技术探讨《清宫医案集成》治疗不寐的用药规律

目的:以数据挖掘技术为基础,探讨《清宫医案集成》中治疗不寐的用药和配伍规律。方法:将《清宫医案集成》中治疗不寐的处方进行整理,在古今医案云平台提供的Excel 2010软件模板上建立处方数据库,通过古今医案云平台对数据进行频数统计、关联规则分析、聚类分析及复杂网络分析。结果:共筛选出345首处方、275味药物;经关联规则分析,对纳入的中药数据进行中药配伍关联分析,筛选出共现频次≥40次的中药,同时设置置信度≥0.6、支持度≥0.2,最终得到13条关联规则数据。高频药物间生成5组聚类组合,核心处方包括白术、白芍、茯神、当归、甘草。结论:《清宫医案集成》中清代宫廷御医治疗不寐,具有气血同治、调心安神、重视肝脾的用药特点。

基于数据挖掘的慢性萎缩性胃炎中医证治规律分析

目的采用数据挖掘方法分析慢性萎缩性胃炎中医证治规律,为临床治疗提供参考。方法借助涵盖消化领域多个优势病种的中医药循证文献大数据平台,检索2000年1月1日-2022年7月31日发表的中医药治疗慢性萎缩性胃炎的名医经验、理论探讨及个案报道类文献,依据纳入、排除标准筛选医案,对医案的中医病名、临床症状、舌象、脉象、中医证候、中医治法等术语进行数据规范及统计分析。结果共纳入文献169篇,收录医案228则,涉及患者228例,累计诊次361次,平均年龄(53.17±11.11)岁,以50~60岁患者最多。慢性萎缩性胃炎多属中医学“胃痛”“痞满”等范畴,主要症状为胃痛、脘痞、嗳气,舌象常见舌黯红、苔白、苔薄或腻,脉象常见弦滑、弦细和弦,高频证型有肝胃气滞证、脾胃湿热证、肝郁脾虚证。病位主要在胃、脾、肝,病性证素多为气虚、气滞、血瘀、热、湿等,复合病性多于单一病性,其中二病性证素以“气虚+气滞”最常见,治法以补益法、理气法、理血法最为常用。结论中医治疗慢性萎缩性胃炎多从胃、脾、肝论治,核心证素为气虚、气滞、血瘀,治疗多用益气健脾、疏肝和胃、调气和血之法。

基于数据驱动中医证治规律研究的核心问题及解决策略

辨证论治是中医核心诊疗思维,是决定临床疗效的关键。如今,基于临床数据研究是探索中医证治规律的主要方法,但未真正而全面地剖析出“病-证-方-药-效”关键因素的内在关系,导致研究结果的临床价值较低。因此,笔者系统梳理了电子病历与临床研究匹配性差、数据治理影响数据准确性、数据分析方法难以发掘中医证治规律等核心问题,并在数据驱动的背景下,建立中医临床科研大数据平台、开发以人工智能为核心的数据治理与分析技术,从而实现临床科研一体化,为中医证治规律研究提供新思路与方法,推动中医药的发展。

中医药治疗儿童咳嗽变异性哮喘用药规律研究

[目的]通过古今医案云平台进行数据挖掘,分析中医药治疗儿童咳嗽变异性哮喘(CVA)的用药规律。[方法]全面检索自建库至2021年12月31日在中国生物医学文献服务系统(SinoMed)、中国知网(CNKI)、维普数据库(VIP)、万方数据知识服务平台(WanFang)中收录的中医药治疗儿童CVA的相关文献,将相关中药处方导入Excel2018并建立标准化数据库,最后通过古今医案云平台软件进行频数分析、中药属性分析、关联规则分析、聚类分析等数据挖掘处理,分析中医药治疗儿童CVA的用药规律。[结果]研究共纳入文献190篇,录入方剂214首,合计中药203味。频数分析显示排名前10位的中药分别为苦杏仁、甘草、炙麻黄、半夏、地龙、蝉蜕、紫苏子、黄芩、桔梗、五味子,药性以温居多,药味以辛为首,归经以肺、脾、胃、肝为主。关联规则分析得出药物配伍规律26条,演化得到核心组合4组。[结论]现代中医药治疗儿童CVA多选用辛温之品,从肺、脾、胃出发,采用宣肺、祛痰、降气、平喘之法。

基于《中华医典》挖掘清代古籍治疗腹痛的用药规律

目的:对清代古籍中腹痛医案的用药进行数据挖掘研究,以分析清代医家治疗腹痛的组方用药规律,了解该时期的学术思想并为临床实践提供经验。方法:以第5版《中华医典》为资料来源,检索清朝时期治疗腹痛的有效处方并建立数据库,运用古今医案云平台(V2.3.5)所包含的关联规则、聚类分析及复杂网络分析系统等数据挖掘方法,归纳出治疗腹痛处方的高频药物、药物组合规律及核心处方。结果:共筛选出196首有效处方,使用中药202味,其中频次大于10次的中药共有34味,以甘草、白芍、当归、白术、茯苓、陈皮使用频次较高,共总结了4组高频药物,常用关联药物组合22对,核心处方1个。结论:清代治疗腹痛多以补虚药、温里药、理气药等为主,药物以甘温为主,归经多入脾、胃经,以厚朴-陈皮,白术-茯苓,陈皮-白术,茯苓-陈皮,苍术-甘草,当归-白芍等药对相互配伍,运用温中补虚、理气健脾、清热解毒、燥湿祛痰、养血和血、柔肝缓急等方式,以聚类分析的“清”“和”“温”“补”为治疗大法,并从“脾胃虚寒证”“痰湿阻滞证”“气血失和证”三大证型出发,得到一个新处方,探讨了清代治疗腹痛的处方及用药特点,了解了该时期的思想特色,也为现代中医药治疗腹痛开辟新的思考方式。

基于数据挖掘分析古代医案治疗癫痫用药规律

目的 基于数据挖掘技术总结古代中医药治疗癫痫的用药规律。方法 利用古今医案云平台(V2.3.8)检索自1912年1月1日前的古代癫痫医案,对符合标准的医案提取中药数据,使用Microsoft Excel 2019建立标准医案数据库,进行频数分析、关联规则分析、聚类分析以及复杂网络分析,总结古代医家治疗癫痫的用药规律。结果 共纳入癫痫医案192则,涉及中药244味,累计频数1975次,排名前五位药物为:胆南星(69次,35.94%),陈皮(68次,35.42%),半夏(61次,31.77%),石菖蒲(59次,30.73%),甘草(54次,28.12%)等,药物性味多属温、寒、平性,甘、苦、辛味,归心、肺、肝经。排名前五位中药功效依次为:补虚药、化痰止咳平喘药、清热药、安神药、理气药。关联强度前五位药对为:半夏-陈皮,石菖蒲-胆南星,远志-石菖蒲,钩藤-陈皮,郁金-胆南星等,通过复杂网络分析,得出核心处方组合:陈皮,半夏,胆南星,石菖蒲,远志,甘草,当归,钩藤,茯神,人参,茯苓,郁金,朱砂。结论 古代医家用药谨守扶正祛邪原则,重视补虚药和化痰药的使用,挖掘出以定痫丸化裁的核心方,为癫痫临床用药提供数据支持。

新安程氏医家程门雪治疗妇科疾病用药规律分析

目的:研究《程门雪医案》治疗妇科疾病的临床组方配伍规律及治疗特色。方法:筛选《程门雪医案》治疗妇科疾病的处方42例,提取方药信息,使用Microsoft Excel2021建立数据库,进行频数分析,应用SPSS 26.0对药物性味归经、药物类别、聚类、关联规则进行分析,进行数据挖掘。结果:药物使用频次最高的四味药物分别是白芍、当归、白术、陈皮。性味归经方面,药物以温性药为主,多用苦、甘、辛之药。归经以肝、脾经为主。药类最常用的药物类别为补虚药。聚类分析共有白术-陈皮-白芍-当归,砂仁-法半夏,党参-炙黄芪,杜仲-石斛,香附-橘核-酸枣仁-茯神,桑寄生-川芎,小麦-黄芩-荷叶-远志,柴胡-吴茱萸-茯苓等8对常用药物组合。结论:程老用药灵活变通,诊治疾病时,能层层剖析,辨别疾病本质,对于治疗痛经、闭经等妇科疾病夹杂其他复杂病症有独特的见解,融会贯通“动形以制神”的思想。并且其用药清快简明,善用“甘温除热”之法。

基于《中医方剂大辞典》挖掘古代治疗偏头痛的用药和时代规律

目的:通过收集整理《中医方剂大辞典》中治疗偏头痛的古代方剂,挖掘其用药和时代规律,为临床治疗偏头痛提供参考。方法:从《中医方剂大辞典》中筛选出符合标准的方剂,导入古今医案云平台,统计不同时代药物种类、四气、五味、归经和功效频率,运用复杂网络分析和关联分析挖掘治疗偏头痛的核心中药和药物关联情况。结果:共筛选出266首方剂,涉及337种中药,各年代方药不全相同,但均喜用温性、苦味或辛味,归肺、肝或脾的中药。其中,最核心的中药为川芎、防风和当归,并常用甘草、细辛、防风和川芎相配伍。结论:各年代医家辨治偏头痛用药不尽相同,但其用药思想包括性味归经等基本相同,从汉唐至清代整体一脉相承,总不离祛风、活血、止痛和从肝论治,可为现代临床治疗偏头痛提供重要参考。

基于数据挖掘分析叶天士辛润通络法方剂构成因子

目的:通过对《临证指南医案》中辛润通络法相关医案的分析,提炼辛润通络法的核心构成因子,归纳此治法的组方用药规律。方法:遴选《临证指南医案》中使用辛润通络法的医案,建立辛润通络法医案数据库。采用SPSS 19.0软件对遴选医案的药物结构进行因子分析,用R4.2.1进行聚类分析并绘制可视化图谱;用方剂计量学方法对高频药物的药性、药味、归经、三焦归属进行统计并绘制可视化图谱;使用CytoSpace 5.8软件构建辛润通络-病名-证型网络可视化表达。结果:纳入医案220则,发现辛润通络法共涉药物177味,大于10次的高频用药39味,其中以当归、桃仁应用频数最高,分别为97与90次。选取出现频数大于4次的病名、病证形成病名-证型可视化网络。降维分析后提取出12个有效公因子,对12个公因子进行系统聚类,得到3个方剂构成因子。药物以性温,味平、辛、甘,归脾、肝、肾经,归属中焦、下焦为主。结论:辛润通络法多选用药性温平,药味辛甘的药物,体现“味辛体润”的基本原则;在药物归经和三焦分属上多选用归脾、肝、肾经,归中、下二焦的药物;在体现“味辛体润”的基本原则上,叶天士多考虑升举奇阳与和少阳木火的兼复病因;在润燥养阴的配伍组成上,除应用子仁类养阴润燥外,叶天士还应用石斛清补法与营卫生会之理从阴阳互根论治。

基于数据挖掘总结林创坚治疗慢性非特异性腰痛用药规律

目的:运用数据挖掘总结林创坚教授治疗慢性非特异性腰痛的用药规律。方法:收集林教授2018年4月—2022年4月接诊的慢性非特异性腰痛病例,筛选建立数据库,使用古今医案云平台(V2.3.2)进行数据分析。结果:共纳入203首处方,涉及106味中药,药物记录共2 955条。其中出现频次>25次的高频药物共有24味,药物的药性以温、平为主,药味以辛、甘、苦为主,主要归肝、肾、脾经。关联规则分析显示支持度排行前5的组合是牛膝-独活、桑寄生-独活、藿香-独活、党参-独活、独活-厚朴。聚类分析得到4个高频药物核心聚类组合,复杂网络分析得出核心方药为:独活、秦艽、厚朴、党参、藿香、甘草、牛膝、桑寄生、杜仲、木瓜、威灵仙、白芷。结论:林教授治疗岭南慢性非特异性腰痛强调三因制宜,辨证用药,以燥湿行气健脾、补肝肾强筋骨为原则,兼顾祛风散寒、补益气血,攻补并用,标本兼顾。

基于数据挖掘技术探究中药治疗小儿疱疹性咽峡炎配伍规律

目的:探究小儿疱疹性咽峡炎的中药配伍规律,为本病的临床治疗及新药研发提供前期参考数据。方法:用计算机检索并收集年限从建库至2022年12月期间的中国知网、维普以及万方数据库中有关中医及中西医结合治疗小儿疱疹性咽峡炎的临床研究文献资料,然后通过采用Excel 2019和古今医案云平台对纳入的组方数据进行频次、性味归经、关联规则以及系统聚类分析。结果:最终共纳入40篇有关小儿疱疹性咽峡炎的文献,涉及药材107味药,总频次380次,使用频次最高的前四种单味药是甘草、金银花、连翘和牛蒡子,药性主要为寒性药(46.05%),药味以苦味(51.58%)为主,甘味次之(37.37%);多归于肺经(56.97%),胃经和心经次之(45.79%,33.95%);共得到两药关联规则21条,核心药物组主要是金银花、连翘、黄芩、牛蒡子、甘草等配伍而成;系统聚类分析得到4类高频中药聚类群。结论:小儿疱疹性咽峡炎的核心配伍规律是以清热解毒、疏散风热、利咽止痛药物为主,同时配伍缓和之药以避免大寒之品伤及脾胃。

当代名老中医治疗肝硬化的证治规律

目的:通过数据挖掘方法探索当代名老中医治疗肝硬化的辨治思想及用药特点,为肝硬化的临床治疗提供思路与方法。方法:选取《当代名老中医典型医案集·内科分册》中治疗肝硬化病案,经数据筛选、数据规范等处理,建立标准化医案数据库,并使用中医传承辅助平台V2.5进行数据分析,探索当代名老中医治疗肝硬化的常见证型、常用药物及其属性、常用药物组合以及新方挖掘。结果:共纳入医案44则,涉及方剂87首,中药201味,药性以寒、温、平为主,药味以甘、苦、辛为主,归经依次为肝经、脾经、胃经、肺经、肾经、心经;主证型为肝郁脾虚、瘀血阻络、肝肾阴虚、湿热蕴结、水湿停滞、气滞血瘀;使用频次较高的药物为丹参、茯苓、黄芪、白术、白芍、党参、柴胡、郁金、泽泻、赤芍;高频药物组合14组,其中鳖甲-丹参、赤芍-丹参、郁金-丹参、黄芪-茯苓、党参-黄芪等为常用药对;新处方有7首,包括“白芍、郁金、泽泻、柴胡、牛膝”“党参、黄芪、牡丹皮、白术”“茵陈、栀子、白花蛇舌草、金钱草”等。结论:当代名老中医认为肝硬化属本虚标实,以肝脾肾虚损为本,兼以气滞、瘀血、水湿、热毒等有形实邪,治疗强调标本同治,注重寒热并用、性味合参,以调补肝脾肾为基础,同时重用活血化瘀、理气和血、利水渗湿、清热解毒之品以达扶正驱邪之功。

从《临证指南医案》分析新安医家叶天士辨治心系疾病组方规律及思想

目的:基于数据挖掘对新安著作《临证指南医案》辨治心系疾病组方用药规律进行探究,希望对临床用药提供新思路。方法:整理《临证指南医案》治疗心系疾病方剂共81首,利用Microsoft Excel 2016、IBM SPSS Modeler18.0、IBM SPSS Statistics 26.0进行关联分析和聚类分析。结果:共计纳入81收医案处方,涉及135种中药,用药总频次为,高频药物(>10次)共味,最高频次药物为茯苓,其次为半夏、甘草、生姜、人参、桂枝、陈皮、茯神、酸枣仁等,药物药味以辛、甘、苦为主,药性以寒、温为主,归脾、肺、心经为主,关联分析共得到核心药物7组,聚类分析将19味中药分为5类。结论:叶天士治疗心系疾病以养心安神,益木生火,调肝理气,化痰逐饮为主,可供临床参考。

Misdiagnosis Features of Ancient Clinical Records Based on Apriori Algorithm

Objective:To analyze misdiagnosis features in clinical cases of“Classified Medical Cases of Famous Physicians”and“Supplement to Classified Case Records of Celebrated Physicians.”Materials and Methods:Two hundred and five ancient misdiagnosed cases were analyzed in aspects of locations(exterior-interior type,qi-blood type and Zang‑Fu organs type)and patterns(heat-cold type and deficiency-excess type)by Apriori Algorithm Method.Results:The main types of misdiagnosis in those medical casesare as follows::Zang‑Fu location misjudgment,misjudging the interior as the exterior,misjudging deficiency pattern as excess pattern,and misjudging cold pattern as heat pattern.Among them,the most outstanding type is the misjudgment of deficiency–cold pattern as excess–heat pattern.Conclusions:(1)Accurate judgment of location and differentiation of deficiency and excess patterns are the key points in diagnosing the diseases correctly.The confusion of true deficiency–cold and pseudo‑excess–heat pattern should be taken seriously.(2)Data mining on ancient clinical cases offers a new methodology for assisting clinical diagnosis of traditional Chinese medicine.

Application of the Data Mining Algorithm in the Clinical Guide Medical Records

Objective:This study analyzed the data of the medical cases in the book,“Clinical Guide Medical records”using a data mining method,to provide a reference for Ye Tianshi’s academic thoughts.Methods:We used the web version of the ancient and modern medical records cloud platform to complete distribution statistics,association rules,cluster analysis,and complex network analysis of all the medical records in the“Clinical Guide Medical records.”These methods were used to summarize the baseline data and to identify the core relationship between Chinese medicine diseases and Chinese medicine,as well as the Chinese medicine Classification.Results:A total of 2572 medical records,3136 visits,and 2879 prescriptions of 1127 traditional Chinese medicines were included in this study.The most common diseases(such as hematemesis),syndromes(such as liver–stomach disharmony),symptoms(such as rapid pulse),disease sites(such as gastric cavity),disease properties(such as Yang deficiency),treatment methods(such as activating Yang),and traditional Chinese medicines(such as Poria cocos)were identified.Furthermore,medicines with a warm,flat,cold,sweet,or bitter taste with its effects on the lungs,spleen,and heart were the most common.The observed effects of the drugs included clearing dampness,promoting diuresis,and strengthening the spleen.The association analysis showed that the associations between TCM diseases and traditional Chinese medicines that had a high confidence were“phlegm and fluid retention–Poria cocos,”“diarrhea–Poria cocos,”etc.The cluster analysis showed that traditional Chinese medicines were classified into five categories.The complex network showed the core relationship between nine high-frequency diseases and nine high-frequency traditional Chinese medicine.Conclusion:This study revealed the most important relationships between traditional Chinese medicines diseases and traditional Chinese medicines and classified the most used traditional Chinese medicines.These findings may help the coming generations of doctors to make accurate diagnoses and treat patients effectively and to improve the clinicians’efficacy in clinical diagnosis and treatment.

医院病案信息系统数据挖掘与应用实践

病案信息是医院的重要信息资料,加强病案信息利用可对患者的诊疗效果及预后进行评价,有效提高医院的医疗质量和综合管理水平。本文着力研究病案信息数据挖掘的应用与实践,对病案信息管理和数据挖掘技术相关内容进行分析,探讨数据挖掘技术在病案信息提取与分析中的应用,结合关联规则的数据挖掘分析肝癌患者复发影响因素,探讨其应用效果,为病案信息挖掘与有效利用提供参考。

基于数据挖掘的臁疮古医案内治法用药规律研究

目的通过数据挖掘方法探讨古医案内治臁疮的用药规律。方法运用古今医案云平台和《中华医典》检索中医内治臁疮的有效验案,经规范化数据整理,利用古今医案云平台分析方剂、药物的使用频次、药物的四气五味、归经,并进行聚类分析、复杂网络分析,分析古代医家治疗臁疮的用药规律。结果共筛选出108篇古医案,共157诊次,涉及方剂30首,中草药179味,使用频次最高的方剂为补中益气汤,使用频次前5位的药物为甘草、当归、白术、茯苓、柴胡。药物性味以温平、甘辛居多,主要归脾经、肺经、心经。关联分析共得9个药对组合,支持度较高的是当归-白术和白术-茯苓,聚类及复杂网络分析得到以白术、当归、甘草、柴胡、茯苓、黄芪、陈皮、人参、升麻、党参、白芍、川芎、熟地黄组成的核心处方。结论古医案中治疗臁疮多以补益气血,兼以祛邪为主要治法,体现了中医学标本同治的理论思想。