本文選題：醫(yī)療服務(wù) + 大數(shù)據(jù)��； 參考：《中國(guó)人民解放軍軍事醫(yī)學(xué)科學(xué)院》2016年博士論文

【摘要】：近年來(lái),隨著計(jì)算機(jī)信息化手段的廣泛運(yùn)用,軍隊(duì)衛(wèi)生統(tǒng)計(jì)工作信息化水平不斷提高,通過(guò)構(gòu)建衛(wèi)生統(tǒng)計(jì)門(mén)戶(hù)網(wǎng)站,為總部首長(zhǎng)提供衛(wèi)生統(tǒng)計(jì)查詢(xún)服務(wù),在數(shù)據(jù)利用方面取得了巨大的進(jìn)步。但是,目前的統(tǒng)計(jì)方法和系統(tǒng)還存在統(tǒng)計(jì)指標(biāo)不夠完善、統(tǒng)計(jì)粒度不夠細(xì)、交互式查詢(xún)響應(yīng)速度慢等問(wèn)題,對(duì)輔助決策支撐能力不足�，F(xiàn)階段,我軍已初步實(shí)現(xiàn)全軍醫(yī)療服務(wù)信息的自動(dòng)抓取,僅結(jié)構(gòu)化數(shù)據(jù)每年的抓取量達(dá)數(shù)百億條記錄,軍隊(duì)衛(wèi)生統(tǒng)計(jì)工作已經(jīng)進(jìn)入了大數(shù)據(jù)時(shí)代。而目前的統(tǒng)計(jì)流程和軟件,需要約一周時(shí)間進(jìn)行年度統(tǒng)計(jì)會(huì)審,難以滿(mǎn)足實(shí)際需求。為此,原總后衛(wèi)生部啟動(dòng)了“軍隊(duì)衛(wèi)生統(tǒng)計(jì)創(chuàng)新工程”作為“十二五”全軍衛(wèi)生信息化建設(shè)的重點(diǎn)工作,大數(shù)據(jù)統(tǒng)計(jì)處理方法和技術(shù)是其中的重要支撐。實(shí)現(xiàn)軍隊(duì)醫(yī)療服務(wù)大數(shù)據(jù)的交互式統(tǒng)計(jì)分析,能夠基于海量原始醫(yī)療數(shù)據(jù)提供以“天”為單位的細(xì)粒度統(tǒng)計(jì)模式,為總部機(jī)關(guān)衛(wèi)勤決策提供數(shù)據(jù)支持,從而及時(shí)掌握醫(yī)療資源的分布和利用情況,快速應(yīng)對(duì)和處置公共突發(fā)衛(wèi)生事件,以及加強(qiáng)對(duì)醫(yī)療服務(wù)機(jī)構(gòu)的指導(dǎo)、管理和監(jiān)督。同時(shí),也可以為軍隊(duì)、國(guó)家的衛(wèi)生統(tǒng)計(jì)系統(tǒng)和區(qū)域醫(yī)療平臺(tái)的建設(shè)提供普適性的方法論指導(dǎo),為構(gòu)建全軍醫(yī)療大數(shù)據(jù)服務(wù)平臺(tái)提供技術(shù)支撐,從而促進(jìn)衛(wèi)勤管理保障從粗放型到精細(xì)型的模式創(chuàng)新。本文運(yùn)用文獻(xiàn)研究法、對(duì)比分析法、專(zhuān)家咨詢(xún)法、系統(tǒng)分析法、調(diào)查法、實(shí)證研究法等研究方法,分析了軍內(nèi)外衛(wèi)生統(tǒng)計(jì)的發(fā)展現(xiàn)狀,對(duì)相關(guān)理論及概念、軍隊(duì)醫(yī)療服務(wù)大數(shù)據(jù)的來(lái)源范疇、數(shù)據(jù)特征進(jìn)行了定義和歸納總結(jié),構(gòu)建了軍隊(duì)衛(wèi)生統(tǒng)計(jì)指標(biāo)體系框架,圍繞大數(shù)據(jù)時(shí)代下的軍隊(duì)醫(yī)療服務(wù)數(shù)據(jù)統(tǒng)計(jì)、分析及利用的功能和性能需求,針對(duì)全軍衛(wèi)生信息中心采用“數(shù)據(jù)直報(bào)”系統(tǒng)從全軍200余家中心醫(yī)院抽取的大樣本分布式、同構(gòu)、結(jié)構(gòu)化、復(fù)雜關(guān)聯(lián)的數(shù)據(jù)進(jìn)行交互式統(tǒng)計(jì)的處理方法和步驟進(jìn)行了梳理總結(jié),并提出了一套基于Spark的并行計(jì)算解決方案,對(duì)數(shù)據(jù)預(yù)處理、分布式存儲(chǔ)、交互式智能統(tǒng)計(jì)和多維可視化等功能模塊所需的關(guān)鍵技術(shù)進(jìn)行了技術(shù)選型,完成了軍隊(duì)醫(yī)療服務(wù)大數(shù)據(jù)交互式分析平臺(tái)系統(tǒng)的架構(gòu)設(shè)計(jì),以Spark計(jì)算平臺(tái)為基礎(chǔ)進(jìn)行了系統(tǒng)原型的實(shí)現(xiàn),并在此基礎(chǔ)上使用不同數(shù)據(jù)規(guī)模的6個(gè)測(cè)試數(shù)據(jù)集和8個(gè)節(jié)點(diǎn)規(guī)模的Spark集群對(duì)原型系統(tǒng)的功能和性能進(jìn)行了對(duì)比和驗(yàn)證。1.勤務(wù)需求分析從衛(wèi)勤保障的勤務(wù)需求出發(fā),分析基于醫(yī)療服務(wù)大數(shù)據(jù)的統(tǒng)計(jì)分析平臺(tái)需具備的功能指標(biāo)和性能指標(biāo)。一是對(duì)軍隊(duì)醫(yī)療服務(wù)數(shù)據(jù)統(tǒng)計(jì)的相關(guān)概念、基礎(chǔ)理論和國(guó)內(nèi)外研究發(fā)展與現(xiàn)狀進(jìn)行了研究,將其歸納為“大樣本復(fù)雜關(guān)聯(lián)數(shù)據(jù)”;二是系統(tǒng)分析了醫(yī)療服務(wù)大數(shù)據(jù)的來(lái)源、范疇及特征;三是從業(yè)務(wù)角度對(duì)現(xiàn)有軍隊(duì)衛(wèi)生統(tǒng)計(jì)指標(biāo)進(jìn)行歸類(lèi)整理,構(gòu)建出了包含業(yè)務(wù)領(lǐng)域、業(yè)務(wù)主題、統(tǒng)計(jì)目的、統(tǒng)計(jì)維度和分析指標(biāo)等5個(gè)層次的軍隊(duì)衛(wèi)生統(tǒng)計(jì)指標(biāo)體系框架,并對(duì)醫(yī)療服務(wù)業(yè)務(wù)領(lǐng)域中的門(mén)診、住院等業(yè)務(wù)主題進(jìn)行了細(xì)化;四是提出了交互式統(tǒng)計(jì)平臺(tái)的功能及性能需求。2.交互式統(tǒng)計(jì)關(guān)鍵技術(shù)選型在勤務(wù)需求分析的基礎(chǔ)上,分析醫(yī)療服務(wù)大數(shù)據(jù)交互式統(tǒng)計(jì)平臺(tái)的數(shù)據(jù)通用處理流程,確定需要分布式存儲(chǔ)、NoSQL數(shù)據(jù)庫(kù)、通用大數(shù)據(jù)處理平臺(tái)和大數(shù)據(jù)可視化Web框架等關(guān)鍵技術(shù),對(duì)各類(lèi)技術(shù)的優(yōu)缺點(diǎn)進(jìn)行對(duì)比分析,借鑒其在互聯(lián)網(wǎng)、金融、電商及醫(yī)療服務(wù)行業(yè)中的具體應(yīng)用,結(jié)合醫(yī)療服務(wù)大數(shù)據(jù)的特點(diǎn),選取適用于交互式統(tǒng)計(jì)分析的技術(shù)組合,即選用Sqoop為醫(yī)療服務(wù)數(shù)據(jù)提供支持增量更新的ETL服務(wù),HDFS和HBase為醫(yī)療服務(wù)大數(shù)據(jù)和其計(jì)算結(jié)果集提供存儲(chǔ)服務(wù),Spark計(jì)算框架提供交互式、高效的并行計(jì)算服務(wù),Web2py提供多維可視化展示。3.醫(yī)療服務(wù)大數(shù)據(jù)交互式統(tǒng)計(jì)平臺(tái)系統(tǒng)設(shè)計(jì)通過(guò)對(duì)醫(yī)療服務(wù)大數(shù)據(jù)交互式統(tǒng)計(jì)分析平臺(tái)建設(shè)目標(biāo)的梳理對(duì)平臺(tái)進(jìn)行架構(gòu)設(shè)計(jì),將體系結(jié)構(gòu)在功能上劃分為外部數(shù)據(jù)接入和存儲(chǔ)、多范式數(shù)據(jù)分析和提取、交互查詢(xún)和數(shù)據(jù)展示三個(gè)基本模塊。從數(shù)據(jù)預(yù)處理和存儲(chǔ)、高效并行計(jì)算服務(wù)和可視化展示三方面分別設(shè)計(jì)相應(yīng)的體系結(jié)構(gòu)和算法。4.系統(tǒng)原型實(shí)現(xiàn)及驗(yàn)證應(yīng)用前面部分的研究成果,指導(dǎo)系統(tǒng)原型設(shè)計(jì)、開(kāi)發(fā)環(huán)境選擇和部署運(yùn)行,以Spark計(jì)算平臺(tái)為基礎(chǔ)對(duì)設(shè)計(jì)的醫(yī)療服務(wù)大數(shù)據(jù)交互式分析平臺(tái)進(jìn)行了系統(tǒng)原型的實(shí)現(xiàn),驗(yàn)證了系統(tǒng)的功能。在此基礎(chǔ)上,以門(mén)診流程所涉及到的相關(guān)數(shù)據(jù)表為例,使用線(xiàn)性增長(zhǎng)的6個(gè)不同大小的測(cè)試數(shù)據(jù)集和8個(gè)節(jié)點(diǎn)的Spark集群對(duì)系統(tǒng)的功能和性能進(jìn)行了對(duì)比測(cè)試驗(yàn)證。測(cè)試的計(jì)算類(lèi)型包括簡(jiǎn)單分組規(guī)約、求和規(guī)約和多表連接等統(tǒng)計(jì)過(guò)程中的代表性操作。利用支持增量更新的數(shù)據(jù)ETL工具Sqoop、分布式文件系統(tǒng)HDFS、分布式數(shù)據(jù)庫(kù)HBase、基于內(nèi)存計(jì)算的Spark框架和簡(jiǎn)單高效的Web2py可視化展示平臺(tái)等大數(shù)據(jù)技術(shù)組合,開(kāi)發(fā)的軍隊(duì)醫(yī)療服務(wù)大數(shù)據(jù)交互式統(tǒng)計(jì)分析平臺(tái)系統(tǒng)原型能夠支持億級(jí)記錄以上醫(yī)療服務(wù)數(shù)據(jù)規(guī)模的交互式統(tǒng)計(jì)查詢(xún),在滿(mǎn)足數(shù)據(jù)預(yù)處理、存儲(chǔ)、計(jì)算和可視化功能的前提下,任務(wù)處理效率能夠隨著硬件節(jié)點(diǎn)資源的增加得到近乎線(xiàn)性的提升。本研究是大數(shù)據(jù)處理技術(shù)在醫(yī)療服務(wù)大數(shù)據(jù)交互式統(tǒng)計(jì)分析中的有益探索和成功嘗試,為建設(shè)全軍范圍內(nèi)的衛(wèi)生信息統(tǒng)計(jì)平臺(tái)以及醫(yī)療服務(wù)大數(shù)據(jù)的進(jìn)一步挖掘和利用提供了第一手的實(shí)踐資料。
[Abstract]:In recent years, with the extensive use of computer information technology, the information level of military health statistics has been improved continuously. Through the construction of health statistics portal, it provides the head head with health statistics inquiry service, and has made great progress in the use of data. However, the statistical methods and systems still have statistical indicators. In the present stage, our army has preliminarily realized the automatic grasping of the medical service information of the whole army, and the volume of structured data has reached hundreds of billions of records every year, and the military health statistics work has entered the era of big data. The statistical process and software need about a week to carry out the annual statistical review, which is difficult to meet the actual demand. Therefore, the former Ministry of health started the "army health statistics innovation project" as the key work of the "12th Five-Year" whole army health information construction, and the major data statistical processing methods and techniques are the important support. The interactive statistical analysis of military medical service data can provide a fine grained statistical model based on the mass original medical data and provide data support for the decision-making of health service in headquarters, so as to timely grasp the distribution and utilization of medical resources, quickly deal with and deal with public emergency health events, and strengthen the public health services. The guidance, management and supervision of medical service institutions can also provide universal methodological guidance for the army, the national health statistics system and the construction of the regional medical platform, and provide technical support for the construction of the whole military medical large data service platform, thus promoting the maintenance of medical service from extensive to fine pattern innovation. By using the methods of literature research, comparative analysis, expert consultation, system analysis, investigation, and empirical research, this paper analyzes the development status of health statistics at home and abroad, defines and summarizes the related theories and concepts, the source category of large military medical service data, and summarizes the data characteristics, and constructs the military health statistics. The framework of the index system is based on the data statistics, analysis and utilization of military medical services in the era of large data, and the large sample distributed, isomorphic, structured and complex data collected by the whole army health information center using "data direct reporting" system from more than 200 central hospitals in the army. The processing methods and steps are summarized, and a set of parallel computing solutions based on Spark is proposed. The key technologies needed for data preprocessing, distributed storage, interactive intelligent statistics and multidimensional visualization are selected, and the interactive analysis platform system of military medical service large data is completed. The architecture design is implemented on the basis of Spark computing platform. On this basis, the function and performance of the prototype system are compared with 6 test data sets of different data scale and the Spark cluster of 8 node scale. The analysis of.1. service requirement analysis is based on the service requirements of the medical service support. The statistical analysis platform for the large data of medical service needs the functional indicators and performance indicators. First, the relevant concepts of military medical service data statistics, basic theory and the development and status of research and development at home and abroad are studied, and it is summed up as "large sample complex association data", and two is a systematic analysis of the source of large data for medical services. Category and characteristics; three is to classify the existing military health statistical indicators from the business point of view, and build a framework of military health statistics index system which includes 5 levels, including business domain, business theme, statistical purpose, statistical dimension and analysis index, and the business topics such as out-patient and hospitalization in medical service business area are carried out. Four is the function and the performance requirement of the interactive statistical platform. The.2. interactive statistical key technology selection is based on the analysis of the service demand. It analyzes the data general processing flow of the interactive Statistical Platform of medical service large data, and determines the need for distributed storage, NoSQL database, general large data processing platform and large data. In view of the key technologies such as Web framework and other key technologies, the advantages and disadvantages of various technologies are compared and analyzed, and the specific applications in the Internet, finance, e-commerce and medical services are used for reference, and combined with the characteristics of the large data of medical services, the technical combination suitable for interactive statistical analysis is selected, that is to choose Sqoop to provide more support for the medical service data. New ETL services, HDFS and HBase provide storage services for medical service large data and its computing result set, Spark computing framework provides interactive, efficient parallel computing services, Web2py provides multidimensional visualization display,.3. medical service large data interactive statistical platform system design through interactive statistical analysis of medical service large data The system structure is divided into external data access and storage, multi paradigm data analysis and extraction, interactive query and data display three basic modules. The corresponding system is designed from three aspects: data preprocessing and storage, efficient parallel computing service and visual display. The structure and algorithm.4. system prototype implements and validates the research results in the front part of the system, directing the system prototype design, developing environment selection and deploying operation. Based on the Spark computing platform, the system prototype is realized and the function of the system is verified. The related data table involved in the diagnosis process is used as an example. Using 6 different test data sets of linear growth and the Spark cluster of 8 nodes, the function and performance of the system are tested and verified. The calculation types of the test include the representative operation in the statistical process, such as the simple packet specification, the request and the protocol and the multi table connection. Support incremental update data ETL tools Sqoop, distributed file system HDFS, distributed database HBase, Spark framework based on memory computing and simple and efficient Web2py visualization display platform and other large data technology combinations, the prototype of interactive statistical analysis platform system for military medical service large data is developed to support more than 100 million records On the premise of meeting the functions of data preprocessing, storage, computing and visualization, the efficiency of task processing can be improved linearly with the increase of hardware node resources. This study is a useful exploration of large data processing technology in the interactive statistical analysis of medical service large data. The first hand is provided for the construction of the health information statistics platform in the whole army and the further mining and utilization of the large data of medical service.

【學(xué)位授予單位】：中國(guó)人民解放軍軍事醫(yī)學(xué)科學(xué)院
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類(lèi)號(hào)】：R82

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