發(fā)布時間：2018-04-24 20:23

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

【摘要】：近年來,隨著計算機信息化手段的廣泛運用,軍隊衛(wèi)生統(tǒng)計工作信息化水平不斷提高,通過構建衛(wèi)生統(tǒng)計門戶網(wǎng)站,為總部首長提供衛(wèi)生統(tǒng)計查詢服務,在數(shù)據(jù)利用方面取得了巨大的進步。但是,目前的統(tǒng)計方法和系統(tǒng)還存在統(tǒng)計指標不夠完善、統(tǒng)計粒度不夠細、交互式查詢響應速度慢等問題,對輔助決策支撐能力不足�，F(xiàn)階段,我軍已初步實現(xiàn)全軍醫(yī)療服務信息的自動抓取,僅結構化數(shù)據(jù)每年的抓取量達數(shù)百億條記錄,軍隊衛(wèi)生統(tǒng)計工作已經(jīng)進入了大數(shù)據(jù)時代。而目前的統(tǒng)計流程和軟件,需要約一周時間進行年度統(tǒng)計會審,難以滿足實際需求。為此,原總后衛(wèi)生部啟動了“軍隊衛(wèi)生統(tǒng)計創(chuàng)新工程”作為“十二五”全軍衛(wèi)生信息化建設的重點工作,大數(shù)據(jù)統(tǒng)計處理方法和技術是其中的重要支撐。實現(xiàn)軍隊醫(yī)療服務大數(shù)據(jù)的交互式統(tǒng)計分析,能夠基于海量原始醫(yī)療數(shù)據(jù)提供以“天”為單位的細粒度統(tǒng)計模式,為總部機關衛(wèi)勤決策提供數(shù)據(jù)支持,從而及時掌握醫(yī)療資源的分布和利用情況,快速應對和處置公共突發(fā)衛(wèi)生事件,以及加強對醫(yī)療服務機構的指導、管理和監(jiān)督。同時,也可以為軍隊、國家的衛(wèi)生統(tǒng)計系統(tǒng)和區(qū)域醫(yī)療平臺的建設提供普適性的方法論指導,為構建全軍醫(yī)療大數(shù)據(jù)服務平臺提供技術支撐,從而促進衛(wèi)勤管理保障從粗放型到精細型的模式創(chuàng)新。本文運用文獻研究法、對比分析法、專家咨詢法、系統(tǒng)分析法、調查法、實證研究法等研究方法,分析了軍內外衛(wèi)生統(tǒng)計的發(fā)展現(xiàn)狀,對相關理論及概念、軍隊醫(yī)療服務大數(shù)據(jù)的來源范疇、數(shù)據(jù)特征進行了定義和歸納總結,構建了軍隊衛(wèi)生統(tǒng)計指標體系框架,圍繞大數(shù)據(jù)時代下的軍隊醫(yī)療服務數(shù)據(jù)統(tǒng)計、分析及利用的功能和性能需求,針對全軍衛(wèi)生信息中心采用“數(shù)據(jù)直報”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、結構化、復雜關聯(lián)的數(shù)據(jù)進行交互式統(tǒng)計的處理方法和步驟進行了梳理總結,并提出了一套基于Spark的并行計算解決方案,對數(shù)據(jù)預處理、分布式存儲、交互式智能統(tǒng)計和多維可視化等功能模塊所需的關鍵技術進行了技術選型,完成了軍隊醫(yī)療服務大數(shù)據(jù)交互式分析平臺系統(tǒng)的架構設計,以Spark計算平臺為基礎進行了系統(tǒng)原型的實現(xiàn),并在此基礎上使用不同數(shù)據(jù)規(guī)模的6個測試數(shù)據(jù)集和8個節(jié)點規(guī)模的Spark集群對原型系統(tǒng)的功能和性能進行了對比和驗證。1.勤務需求分析從衛(wèi)勤保障的勤務需求出發(fā),分析基于醫(yī)療服務大數(shù)據(jù)的統(tǒng)計分析平臺需具備的功能指標和性能指標。一是對軍隊醫(yī)療服務數(shù)據(jù)統(tǒng)計的相關概念、基礎理論和國內外研究發(fā)展與現(xiàn)狀進行了研究,將其歸納為“大樣本復雜關聯(lián)數(shù)據(jù)”;二是系統(tǒng)分析了醫(yī)療服務大數(shù)據(jù)的來源、范疇及特征;三是從業(yè)務角度對現(xiàn)有軍隊衛(wèi)生統(tǒng)計指標進行歸類整理,構建出了包含業(yè)務領域、業(yè)務主題、統(tǒng)計目的、統(tǒng)計維度和分析指標等5個層次的軍隊衛(wèi)生統(tǒng)計指標體系框架,并對醫(yī)療服務業(yè)務領域中的門診、住院等業(yè)務主題進行了細化;四是提出了交互式統(tǒng)計平臺的功能及性能需求。2.交互式統(tǒng)計關鍵技術選型在勤務需求分析的基礎上,分析醫(yī)療服務大數(shù)據(jù)交互式統(tǒng)計平臺的數(shù)據(jù)通用處理流程,確定需要分布式存儲、NoSQL數(shù)據(jù)庫、通用大數(shù)據(jù)處理平臺和大數(shù)據(jù)可視化Web框架等關鍵技術,對各類技術的優(yōu)缺點進行對比分析,借鑒其在互聯(lián)網(wǎng)、金融、電商及醫(yī)療服務行業(yè)中的具體應用,結合醫(yī)療服務大數(shù)據(jù)的特點,選取適用于交互式統(tǒng)計分析的技術組合,即選用Sqoop為醫(yī)療服務數(shù)據(jù)提供支持增量更新的ETL服務,HDFS和HBase為醫(yī)療服務大數(shù)據(jù)和其計算結果集提供存儲服務,Spark計算框架提供交互式、高效的并行計算服務,Web2py提供多維可視化展示。3.醫(yī)療服務大數(shù)據(jù)交互式統(tǒng)計平臺系統(tǒng)設計通過對醫(yī)療服務大數(shù)據(jù)交互式統(tǒng)計分析平臺建設目標的梳理對平臺進行架構設計,將體系結構在功能上劃分為外部數(shù)據(jù)接入和存儲、多范式數(shù)據(jù)分析和提取、交互查詢和數(shù)據(jù)展示三個基本模塊。從數(shù)據(jù)預處理和存儲、高效并行計算服務和可視化展示三方面分別設計相應的體系結構和算法。4.系統(tǒng)原型實現(xiàn)及驗證應用前面部分的研究成果,指導系統(tǒng)原型設計、開發(fā)環(huán)境選擇和部署運行,以Spark計算平臺為基礎對設計的醫(yī)療服務大數(shù)據(jù)交互式分析平臺進行了系統(tǒng)原型的實現(xiàn),驗證了系統(tǒng)的功能。在此基礎上,以門診流程所涉及到的相關數(shù)據(jù)表為例,使用線性增長的6個不同大小的測試數(shù)據(jù)集和8個節(jié)點的Spark集群對系統(tǒng)的功能和性能進行了對比測試驗證。測試的計算類型包括簡單分組規(guī)約、求和規(guī)約和多表連接等統(tǒng)計過程中的代表性操作。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HBase、基于內存計算的Spark框架和簡單高效的Web2py可視化展示平臺等大數(shù)據(jù)技術組合,開發(fā)的軍隊醫(yī)療服務大數(shù)據(jù)交互式統(tǒng)計分析平臺系統(tǒng)原型能夠支持億級記錄以上醫(yī)療服務數(shù)據(jù)規(guī)模的交互式統(tǒng)計查詢,在滿足數(shù)據(jù)預處理、存儲、計算和可視化功能的前提下,任務處理效率能夠隨著硬件節(jié)點資源的增加得到近乎線性的提升。本研究是大數(shù)據(jù)處理技術在醫(yī)療服務大數(shù)據(jù)交互式統(tǒng)計分析中的有益探索和成功嘗試,為建設全軍范圍內的衛(wèi)生信息統(tǒng)計平臺以及醫(yī)療服務大數(shù)據(jù)的進一步挖掘和利用提供了第一手的實踐資料。
[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.

【學位授予單位】：中國人民解放軍軍事醫(yī)學科學院
【學位級別】：博士
【學位授予年份】：2016
【分類號】：R82

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