1、大數(shù)據(jù)在醫(yī)療行業(yè)的應(yīng)用2議程醫(yī)療與大數(shù)據(jù)的趨勢什么是醫(yī)療大數(shù)據(jù)?大數(shù)據(jù)面臨的挑戰(zhàn)如何管理和利用大數(shù)據(jù)案例分享總結(jié)與展望趨勢分析: 我們正處在醫(yī)療行業(yè)的一個重要轉(zhuǎn)折點醫(yī)療費用在不斷上升GDP的占比非常高Source: U nited Nation s “Po pulati on Agi ng 200 2”25- 29%30+ %20- 24%10- 19%0-9 % % of popu lation over age 602050WW Average Age 60+: 21%全球老齡化 平均年齡60 +: 目前的10%, 到2050年將達到20%以美國為例: 醫(yī)療大數(shù)據(jù)的價值3千億美元/年, 相

2、當于每年生成總值增長0.7%趨勢分析:我們正處在醫(yī)療行業(yè)的一個重要轉(zhuǎn)折點050001000015000201020112012201320142015存儲的增長醫(yī)療服務(wù)產(chǎn)生的數(shù)據(jù)總量(PB)Admin Imaging EMREmail FileNon Clin ImgResearch醫(yī)療影像歸檔一個醫(yī)療系統(tǒng)案例的數(shù)據(jù)到2020年, 醫(yī)療數(shù)據(jù)將急劇增長到35 Zetabytes, 相當于2009年數(shù)據(jù)量的44倍 增長67議程醫(yī)療與大數(shù)據(jù)的趨勢什么是醫(yī)療大數(shù)據(jù)?大數(shù)據(jù)面臨的挑戰(zhàn)如何管理和利用大數(shù)據(jù)案例分享總結(jié)與展望醫(yī)療大數(shù)據(jù)簡介1. 制藥企業(yè)/生命科學(xué)3. 費用報銷, 利用率 和 欺詐監(jiān)管2. 臨

3、床決策支持 & 其他臨床應(yīng)用 (包括診斷相關(guān)的影像信息)4. 患者行為/社交網(wǎng)絡(luò)數(shù)據(jù)來源包括哪些?我們?nèi)绾卫么髷?shù)據(jù)創(chuàng)造價值?(示例)1. 個體化醫(yī)療3. 欺詐監(jiān)測得以加強2. 臨床決策支持4. 由生活方式和行為引發(fā)的疾病分析8McKinsey Global Institute Analysis醫(yī)療大數(shù)據(jù)相關(guān)解決方案分布式平臺存儲優(yōu)化安全和隱私影像數(shù)據(jù)處理加速新興的醫(yī)療服務(wù) 應(yīng)用個體化醫(yī)療臨床決策支持腫瘤基因組學(xué)健康信息服務(wù)基礎(chǔ)醫(yī)療服務(wù)個人健康管理老齡社會數(shù)據(jù)分析及 視覺化處理類SQL的檢索醫(yī)療影像分析機器學(xué)習(xí)數(shù)據(jù)處理/ 管理醫(yī)療影像醫(yī)療記錄基因數(shù)據(jù)910議程醫(yī)療與大數(shù)據(jù)的趨勢什么是醫(yī)療大數(shù)據(jù)

4、?大數(shù)據(jù)面臨的挑戰(zhàn)如何管理和利用大數(shù)據(jù)案例分享總結(jié)與展望大數(shù)據(jù)的挑戰(zhàn)不僅來自于數(shù)據(jù)量的增長.需要新技術(shù)的支持檢驗結(jié)果, 費用數(shù)據(jù), 影像, 設(shè)備產(chǎn)生的感應(yīng)數(shù)據(jù), 基因數(shù)據(jù)等數(shù)據(jù)量結(jié)構(gòu)化數(shù)據(jù), 遵循標準的數(shù)據(jù)標準(如,HL7)非結(jié)構(gòu)化數(shù)據(jù), 如口述、手寫、照片、影像等類型在傳統(tǒng)的解決方案之上，引入新的數(shù)據(jù)及分析模型和技術(shù)， 實時有效的商業(yè)價值基于現(xiàn)有數(shù)據(jù)庫中的數(shù)據(jù)進行分析，來支持不同種類的業(yè)務(wù)：如 費用及報銷、患者病史、歸檔影像分析、實時臨床決策支持（數(shù) 據(jù)分析）價值實時數(shù)據(jù)分析，而非傳統(tǒng)的批量處理分析數(shù)據(jù)以流的方式進入系統(tǒng)，進行抽取和分析對于實時運行中的每個時間節(jié)點產(chǎn)生影響，而不是事后處理速度

5、1112議程醫(yī)療與大數(shù)據(jù)的趨勢什么是醫(yī)療大數(shù)據(jù)?大數(shù)據(jù)面臨的挑戰(zhàn)如何管理和利用大數(shù)據(jù)案例分享總結(jié)與展望關(guān)注數(shù)據(jù)的價值數(shù)據(jù)源文本-語音-視頻-傳感器Requesting Or M2M通訊批量 商業(yè)應(yīng)用傳統(tǒng)解決方案 環(huán)境ERP, CRM, Batch, OLTP-DB邊緣服務(wù)器(Edge)大數(shù)據(jù)存儲的考慮 傳統(tǒng)存儲方式大規(guī)模數(shù)據(jù)分析 Hadoop*海量數(shù)據(jù)庫 Hive*大規(guī)模備份 Lustre*豐富的視覺化效果 安全的數(shù)據(jù)分析和緩存分析 同步 端到端Machine-to-Machine Source-to-Source13關(guān)注數(shù)據(jù)的價值數(shù)據(jù)源文本-語音-視頻-傳感器Requesting Or M2

6、M通訊批量 商業(yè)應(yīng)用傳統(tǒng)解決方案 環(huán)境ERP, CRM, Batch, OLTP-DB邊緣服務(wù)器（Edge）Data Center ProvisioningDiscrete VirtualCloud As A Service HPC大數(shù)據(jù)存儲的考慮 傳統(tǒng)存儲方式大規(guī)模分析 Hadoop* 海量數(shù)據(jù)庫 Hive* 大規(guī)模備份 Lustre*豐富的視覺化效果 安全的數(shù)據(jù)分析和緩存分析 同步 端到端Machine-to-Machine Source-to-Source可行的解決方案體系（示例）Applications & ServicesVisualization File Structure &

7、Analytical ToolsData Delivery, Operational & Graphical AnalyticsData Management & Computational AnalyticsCompute Storage & Infrastructure Platforms14大數(shù)據(jù)解決方案的部署方式（參考）企業(yè)級數(shù)據(jù)倉庫電子表格視覺化工具數(shù)據(jù)挖掘集成開發(fā)工具ODS & 數(shù)據(jù)集市企業(yè)應(yīng)用工具傳統(tǒng)的文件格 式日志社交 & 網(wǎng)絡(luò)遺留系統(tǒng)結(jié)構(gòu)化非結(jié)構(gòu)化錄音文件& 筆記等數(shù)據(jù)平臺關(guān)系型數(shù)據(jù)庫No-SQL內(nèi)存數(shù)據(jù)庫SQL應(yīng)用NodeNodeNodeHadoop*Web Apps Ma

8、shUpsIMPORTINSIGHTSCONSUMECreate MapREDUCE1516大數(shù)據(jù)解決方案的整體框架架構(gòu)Data VelocityProvisioning Models-Storage & Connectivity ConsiderationsMPP DatabasesDW AppliancesDatabases DBMS / NoSQL10GBeFast FabricData VulnerabilityNAS - SAS and DistributedMedical DevicesStorageData SourcesText, VideoSecurity Services

9、Privacy ComplianceHuman Genome & Drug DiscoveryGISSurveillance and Medical Device Streaming DataDiagnostic ImagesSocial MediaMedical RecordsLog Filesand AudioProvisioning Models Can Vary by Data Characteristics17大數(shù)據(jù)解決方案的整體框架架構(gòu)Data as a ServicesData VelocityData Volume andQualityIntegration ToolsDist

10、ributed High Performance Data ProcessingHadoop* MapReduceData ingestion, Integration and Processing ServicesProvisioning Models-Storage & Connectivity ConsiderationsMPP DatabasesDW AppliancesDatabases DBMS / NoSQL10GBeFast FabricVertically Integrated Software IntelAIMSuiteData VulnerabilityHPC / TCP

11、 MICNAS - SAS and DistributedData CharacteristicsDistributedVirtualPersistenceEvent, Message Real-Time, Cached, Federated EDW, MartsCloudMedical DevicesStorageData SourcesText, VideoSecurity Services Privacy ComplianceHuman Genome & Drug DiscoveryGISSurveillance and Medical Device Streaming DataDiag

12、nostic ImagesSocial MediaMedical RecordsLog Filesand AudioProvisioning Models Can Vary by Data Characteristics18大數(shù)據(jù)解決方案的整體框架架構(gòu)Data as a ServicesBI & Predictive AnalyticsExisting BI/Analytics with in-databasedata processing supportData VelocityData Volume and QualityIntegrated Analytics with Hadoop S

13、upportIntegration ToolsDistributed High Performance Data ProcessingHadoop* MapReduceData ingestion, Integration and Processing ServicesProvisioning Models-Storage & Connectivity ConsiderationsMPP DatabasesDW AppliancesDatabases DBMS / NoSQLCustom Analytic SolutionsMapReduceTextual AnalyticsStreaming

14、 Analytics10GBeFast FabricVertically Integrated Software IntelAIMSuiteNLP/Semantic Search/ Machine Learning Knowledge ManagementData VulnerabilityHPC / TCP MICNAS - SAS and DistributedData Access User AuthenticationData CharacteristicsDistributedVirtualPersistenceEvent, Message Real-Time, Cached, Fe

15、derated EDW, MartsData VisibilityCloudMedical DevicesStorageData SourcesText, VideoSecurity Services Privacy ComplianceHuman Genome & Drug DiscoveryGISSurveillance and Medical Device Streaming DataDiagnostic ImagesSocial MediaMedical RecordsLog Filesand AudioProvisioning Models Can Vary by Data Char

16、acteristics高效的大數(shù)據(jù)訪問途徑 (客戶端)“Express Me”智能手機移動醫(yī)療 助理平板電腦筆記本, Ultrabook 其他設(shè)備臺式機數(shù)字標牌自助終端MobilityVital sign, I & O entryMedication administrationTemplate data entryFree-format textdata entryLarge diagnosticimagesData inquiryManageability“Know Me”“Free Me”“Link Me”19產(chǎn)業(yè)鏈合作主流的數(shù)據(jù)庫和數(shù)據(jù)分析軟件都基于英特爾平臺進行了充分優(yōu)化無論您選擇哪

17、種解決方案：都已經(jīng)基于 Intel Xeon 處理器及平臺進行了優(yōu)化Database and compute infrastructureAnalytics enginesRelationalNonrelationalVOLTDBEXALYTICSLife Sciences Workloads & SolutionsOpen Source: BLAST, FASTA,ClustalW, HMMER, Darwin, etc.20英特爾產(chǎn)品和軟件為大數(shù)據(jù)提供支持網(wǎng)絡(luò)Intelligent scale-out networkingfrom 10GBe 40GBe高性能客戶端Rich modelin

18、g support Client server application management高速架構(gòu) & 緩存 Investing in new fabric approachesnon-volatile memory that provide capacity caching for data velocity英特爾軟件生態(tài)鏈 Hadoop* Lustre*In-memoryIn stream data analysis End to end security計算Intel Xeon processor E5- and E7 based servers up to 80% performan

19、ce boost with hardware-enhanced security存儲Intelligent scale-out storage built with Intel Xeon processor E5-based storageTechnical Compute Intel Xeon processor E5- based servers for TCP Intel Xeon Phi co- processorIntegrated Systems Embedded Analysis Solutions From Intel ISG 可擴展靈活動態(tài)的工作負載和分 析處理能力充分優(yōu)化

20、數(shù) 據(jù)交付和管理軟 件產(chǎn)業(yè)鏈支持高效互聯(lián)穩(wěn)定和安全的互聯(lián)技術(shù)可視化、移動 高性能客戶端 豐富的視覺展現(xiàn) 無縫的訪問體驗21基于英特爾架構(gòu)的大數(shù)據(jù)處理和分析服務(wù)器The Dell | Cloudera* solution for Apache* Hadoop combinesCisco* UCS Server1 Intel Xeon processor 5600Dell* PowerEdge* C Series2 Intel Xeon processor 5500/5600Cisco UCS server with EMC Greenplum MR software - “enterprise-

21、class”Hadoop* distribution that features technology from MapROracle* Sun Fire* server3 Intel Xeon processor E7- 480022Oracle Exalytics* In-Memory Machine, features the Oracle BI Foundation Suite and Oracle TimesTen In-Memory Database for ExalyticsPerformance comparison using best submitted/published

22、 2-socket server results on the SPECfp*_rate_base2006 benchmark as of 6 March 2012. Baseline score of 271 published by Itautec on the Servidor Itautec MX203* and Servidor Itautec MX223* platforms based on the prior generation Intel Xeon processor X5690. New score of 492 submitted for publication by

23、Dell on the PowerEdge T620 platform and Fujitsu on the PRIMERGY RX300 S7* platform based on the Intel Xeon processor E5-2690. For additional details, please visit . Intel does not control or audit the design or implementation of third party benchmark data or Web sites referenced in this document. In

24、tel encourages all of its customers to visit the referenced Web sites or others where similar performance benchmark data are reported and confirm whether the referenced benchmark data are accurate and reflect performance of systems available for purchase.1 /cloud/ciscos-servers-now-tuned-for-hadoop/

25、2 /news/home/20110804005376/en/Dell-Cloudera-Collaborate-Enable-Large-Scale-Data3 /mobile/588145-oracle-unveils-exalytics-in-memory-machine大數(shù)據(jù)在中國醫(yī)療行業(yè)中的應(yīng)用模式1.制藥企業(yè)/生命科 學(xué)233.費用報銷, 利用 率 和 欺詐監(jiān)管4.患者行為/社交 網(wǎng)絡(luò)2.臨床決策支持 & 其他臨床應(yīng)用 (包 括診斷相關(guān)的影像 信息)藥品研發(fā)對藥品實際 作用進行分析；實 施藥品市場預(yù)測基因測序分布式計算加快基因測序計算 效率臨床數(shù)據(jù)比對匹配同類型的病人，用藥臨床決策

26、支持利用規(guī)則和數(shù)據(jù)實時分析給 出智能提示公共衛(wèi)生實時統(tǒng)計分析 發(fā)現(xiàn)公共衛(wèi)生疫情及公民健康 狀況新農(nóng)合基金數(shù)據(jù)分析 及時了解基金狀況，預(yù)測風(fēng)險 輔助制定農(nóng)合基金的起付線， 賠付病種等基本藥物臨床應(yīng)用分析分析基本藥物在處方中的比例遠程監(jiān)控采集并分析病人隨身攜帶儀 器數(shù)據(jù)，給出智能建議人口統(tǒng)計學(xué)分析對不同群體人群的就醫(yī)，健 康數(shù)據(jù)實施人口統(tǒng)計分析了解病人就診行為 發(fā)現(xiàn)病人的特定就診行為， 分配醫(yī)療資源24議程醫(yī)療與大數(shù)據(jù)的趨勢什么是醫(yī)療大數(shù)據(jù)?大數(shù)據(jù)面臨的挑戰(zhàn)如何管理和利用大數(shù)據(jù)案例分享總結(jié)與展望案例分享: Regional Health Info Network ChinaReal-time Cl

27、inical Decision Support實時的醫(yī)療數(shù)據(jù)處理（電子健康檔案，醫(yī) 療影像數(shù)據(jù)），支持醫(yī)療協(xié)同、臨床決策 支持和公共衛(wèi)生管理采用 Hadoop* (HBase*/Hive*)來實現(xiàn)醫(yī) 療數(shù)據(jù)分析和處理未來將擴展到不同領(lǐng)域、不同區(qū)域/地區(qū)（包括數(shù)據(jù)交換、處理和分析）與本地的軟件廠商及OEM廠商進行了廣泛 合作技術(shù)挑戰(zhàn)Hadoop (HBase/Hive)與傳統(tǒng)關(guān)系型數(shù)據(jù) 庫如何有效結(jié)合大數(shù)據(jù)在區(qū)域衛(wèi)生信息平臺中的切實可行 應(yīng)用場景Public HealthHospitalPrimary care (Grassroots)Ancillary Data & ServicesHealt

28、h Information DWEHRData & ServicesRegistries Data & ServicesLongitudinal Record ServicesHealth Information Access LayerCare Coordination Clinical decision supportData Analytic R&DRHIN25區(qū)域醫(yī)療及基層醫(yī)療信息系統(tǒng)大數(shù)據(jù)解決方案(Hadoop*)分布式數(shù)據(jù)服務(wù)系統(tǒng)展現(xiàn)層(報告, 視圖)集成的用戶應(yīng)用界面（居民、醫(yī)生、衛(wèi)生行政管理人員）數(shù)據(jù)挖掘（Mahout）分布式批量處理框架(Map/Reduce)協(xié)作 服務(wù)(Zo

29、okeeper)結(jié)構(gòu)化數(shù)據(jù)采集器(Sqoop)日志數(shù)據(jù)采集器(Flume)分布式文件系統(tǒng)(HDFS)區(qū)域衛(wèi)生信息訪問層(HIAL)醫(yī)院信息系統(tǒng)醫(yī)院信息系統(tǒng)實時數(shù)據(jù)庫(Hbase)語言和編譯(Hive)基層醫(yī)療信息系 統(tǒng)公共衛(wèi)生醫(yī)療服務(wù)藥品管理新農(nóng)合醫(yī)療保 險服務(wù)器虛擬 化基礎(chǔ)設(shè)施虛擬化網(wǎng)絡(luò)虛擬化存儲虛擬化基于云的區(qū)域基層醫(yī)療服務(wù)系統(tǒng)多租戶應(yīng)用健康檔案數(shù)據(jù)存儲運營管理26Sequencing3 Billion Base PairsData Processing Cloud Storage VisualizationMillions of VariantsInterpretation & Anal

30、yticsMillions of Variants Millions of PatientsCommercializingTargeted Therapeutics Companion DiagnosticsActionable Biomarkers案例分享: NEXTBIO基因數(shù)據(jù)分析Cost to sequence a genome has fallen by 800 x in the last 4 yearsEach genome has 4 million variantsGrowth in the genomics data in the public and private dom

31、ainData available in variety of sourcesStructured, semi-structured, unstructuredNew aggregated data growing exponentially27案例分享: NEXTBIO病人相關(guān)性數(shù)據(jù)Novel DiscoveriesBiomarkers Disease Mechanism Drug IndicationsClinical Trial Parameters Patient Care OptionsLarge content repository of public and private ge

32、nomic data combined with proprietary and patented correlation engine28案例分享: NEXTBIONextbio & Intel 合作方向技術(shù)挑戰(zhàn):Immutable Data write once, never change, read many timesTraditional Bloom Filters worksHadoop* & HBase* well suited1 genome 10 million rows 100 genomes 1billion rows 1M genomes 10 trillion row

33、s100M genomes 1 quadrillion 1,000,000,000,000,000 rowsApp can dynamically partitions HBase as data size grows英特爾對于Hadoop提供的優(yōu)化:Optimized Hadoop stack in open sourceStabilize HBase to provide reliable scalable deploymentOptimize and support scale-out as data size dramatically growsExploring cluster au

34、to tuning, Security & Compliance, etc.2930案例分享: Kaiser Permanente 大數(shù)據(jù)應(yīng)用31數(shù)據(jù)的發(fā)展趨勢結(jié)構(gòu)化數(shù)據(jù)80%非結(jié)構(gòu)化數(shù)據(jù)全世界 80% 的數(shù)據(jù)是非結(jié)構(gòu)化的 (大量的移動 終端設(shè)備, 機器產(chǎn)生的數(shù)據(jù))在未來十年，數(shù)據(jù)將迎來 44 倍的增長 (35 zettabytes by 2020)主要的數(shù)據(jù) 增長 來自于 非結(jié)構(gòu)化數(shù)據(jù) (在線 的歸檔數(shù)據(jù), 醫(yī)療影像, 在線視頻和存儲, 照 片等)信息 給各行業(yè)發(fā)展帶來了新一輪的機遇 (零售, 金融, 保險, 制造, 醫(yī)療,)各行業(yè)已經(jīng)開始采用 大數(shù)據(jù)技術(shù) 用于信息提 取全球數(shù)據(jù)的構(gòu)成Kai

35、ser的數(shù)據(jù)中， 90% 是非結(jié)構(gòu)化的 (80%的EHR和影像數(shù)據(jù))在未來十年，數(shù)據(jù)將會有25 倍的增長 (One exabyte by 2020)主要的數(shù)據(jù) 增長 來自于 非結(jié)構(gòu)化數(shù)據(jù) (醫(yī) 療影像, 視頻, 文本, 音頻等)信息 給 實時個性化醫(yī)療服務(wù)帶來了可能性 (Requires Contextual device, environment, spatial, Demographics, Social and Behavioral profiles in addition to medical information)Kaiser 正在評估大數(shù)據(jù)相關(guān)技術(shù)Kaiser的數(shù)據(jù)構(gòu)成結(jié)構(gòu)化數(shù)據(jù)

36、90%非結(jié)構(gòu)化數(shù)據(jù)Source: Kaiser數(shù)據(jù)平臺計算的趨勢 分布式計算Discontinuous ChangeSAN/NASMasterSlave(s)Data is distributed across processing slave nodesResources containing data are not sharedMaster manages the data distribution, job scheduling across slave nodes and aggregating result setsIntegrate built/bought Real-time

37、Predictive Analytical Solutions or Processing logicSMP (5$)MPP (10$)In-Memory (50$)SAN/NASSAN/NASShare-Nothing Distributed Storage and Compute ($)Fault-tolerant MasterSlave Architecturecapable of withstanding partial system failuresDASSAN/NASSMP (Disk Caching, High Speed Network) (10$)Kaiser is look

38、ing to exploit this capabilityStructured, Relational Tabular DataInteractive Query SupportReal-time AnalyticsSQL Transaction DataUnstructured, Non-tabular DataRich Ad Hoc IntegrationReal-time AnalyticsUQL ALL Data32大數(shù)據(jù)平臺需求分析 Ingestion(Data Model, Metadata Reference Data, Store) Integration(Alignment

39、, Semantics, Completeness, Quality) Interrogation(Clustering, Statistical, Quality, Semantics) Information(Standard & Ad Hoc reporting, Query, Alerts, Forecasting, Access)數(shù)據(jù)量 (Sensors, EMR, Claims, Pharmacy, Images)類型(Structured, Text, Unstructured, Documents, Images)處理的特性 Intuition(Simulation, Opti

40、mization,Stochastic Optimization)A unified information storage methodologyenabling users to manage data from ALL sources.A portfolio of tools to manage (profile, cleanse, classify, synchronize, aggregate, integrate, share) ALL types of data.Support current BI tools focused on structured information.

41、 Build/buy packaged unstructured data processing and analytics tools.Ability to model information and transition from multiple access methods to generating, sharing, collaborating and acting on insights anytime, anywhere on any device.速度 (SLAs, Real-time Decision Support & Contextual Intelligence)In

42、formation drives process optimizations with strategic impact. Modeling business intuition from data deluge.33數(shù)據(jù)的特性大數(shù)據(jù) 界定的標準DATA SIZEDATA TYPE DATA CLASS DATA CATALOG DATA VELOCITY DATA ACCESS DATABASE TYPESERVER ARCHITECTURESTORAGE ARCHITECTUREGigabytes, Terabytes, PetabytesStructured, Semi-Structur

43、ed, Unstructured Human Generated, Machine Generated Text, Image, Audio, VideoBatch, StreamingAnalytics, Search, Transaction (ACID, BASE)Relational , File Based, Columnar, NoSQL, Document, Graph, RDFSMP, MMP, Distributed ProcessingNAS, SAN, Direct Access Storage, Spinning Disks, Flash, SSDFRAMEWORKSF

44、inancial, Computer Vision Engine, Geospatial, Machine Learning,Mathematical, Natural Language Processing, Neural Networks,ANALYTICSStatistical Modeling, Time-Series Analysis, Voice Engine Standard Reporting, Ad hoc Reporting, Query/Drill downs, Alerts Forecasting, Simulations, Optimization, Stochast

45、ic OptimizationsDISTRIBUTED PROCESSINGAppliance, Commodity Cluster (CC) 1K nodes3435議程醫(yī)療與大數(shù)據(jù)的趨勢什么是醫(yī)療大數(shù)據(jù)?大數(shù)據(jù)面臨的挑戰(zhàn)如何管理和利用大數(shù)據(jù)案例分享總結(jié)與展望總結(jié)我們正處在醫(yī)療行業(yè)大數(shù)據(jù) 和分析的一個重要轉(zhuǎn)折點我們需要讓大數(shù)據(jù)更為高效， 可以便捷的訪問專注在創(chuàng)新，依賴產(chǎn)業(yè)鏈來 提供企業(yè)核心能力之外的服 務(wù)采用標準和最佳實踐，參考 全球已有的成熟模型3637展望讓我們一起讓醫(yī)療大數(shù)據(jù)成為現(xiàn)實:提供具有差異化的技術(shù)解決方案，探索開放標準和最 佳實踐尋找可能的客戶和產(chǎn)業(yè)鏈合作伙伴，共同探索醫(yī)療行

46、業(yè)的核心應(yīng)用模式與產(chǎn)業(yè)合作進行驗證，加速大數(shù)據(jù)的采用通過共同的市場活動來提高客戶對于大數(shù)據(jù)的理解和 認知，擴大領(lǐng)先技術(shù)的影響力，并且為客戶創(chuàng)造價值37讓我們一起合作，共同創(chuàng)造網(wǎng)絡(luò)效應(yīng)！關(guān)于這一主題的其它信息，請參照Big Data and Analytics at Intel - Intel Big Data and AnalyticsHealthcare Blogs Intel Healthcare IT ProfessionalsWhitepapersThe Growing Importance of Big Data, Real Time AnalyticsSAP In-Memory A

47、ppliance Software: Real-Time Business IntelligenceOracle: Big Data for EnterpriseBig Data: The next frontier for innovation, competition, and productivityVideosSAP-HANA A Collaboration Between SAP & Intel38Intel Virtualization Technology (Intel VT) Provides flexibility and maximum system utilizationby consolidating multiple environments into a single server, workstation, or PCIntel vPro Technology Designed specifically for the needs of business, notebooks and desktopswith Intel vPro technology have security and manage