




版權(quán)說明:本文檔由用戶提供并上傳,收益歸屬內(nèi)容提供方,若內(nèi)容存在侵權(quán),請(qǐng)進(jìn)行舉報(bào)或認(rèn)領(lǐng)
文檔簡介
1、由大數(shù)據(jù)到智能醫(yī)學(xué)Translational Medicine Two strategic requirementChange of medical modelHealth care reform The engine and bridge from big data tointelligent medicineTranslational MedicineGenomic medicineDigital medicineBig dataFrom bench to bedsideFrom big data to intelligent medicineIntelligent Medicine5P
2、MedicinePredictive medicine Preventive medicinePersonalized medicine Precision medicineParticipatory medicineIntelligent medicine Right person Right time Right treatment Right healthcareBig DataNature 498: 255 (2013)THE BIG CHALLENGES OF BIG DATANature 498: 255 (2013) As they grapple with increasing
3、ly large data sets,biologists and computer scientists uncork newbottlenecks. Biologists are joining the big-data club. With theadvent of high-throughput genomics, life scientistsare starting to grapple with massive data sets,encountering challenges with handling, processingand moving information tha
4、t were once thedomain of astronomers and high-energyphysicists.Biomedical big data Biological data Medical data Real time physiological and pathologicaldateBiological DataStandardizing experimental protocolsCurrent Opinion in Biotechnology 19:354-359(2008) Systems biology aims at understanding the b
5、ehavior ofbiological networks by mathematical modeling based onexperimental data. The procedures of data generation are insufficientlydocumented and data processing is arbitrary. Standardization at multiple levels is essential. One of the key issues is to obtain highly reproduciblequantitative data
6、for mathematical modeling.Standardization of hypothesis-driven research in systems biologyCurrent Opinion in Biotechnology 19:354-359(2008)Integration of complexmultidimensional dataNature 452:553 (2008)Omics Genomics ( metagenomics ) Epigenomics ( Rnomics ) Transcriptomics Proteomics Metabolomics I
7、nteractomics PhenomicsMicrobiome-wide association studies linkdynamic microbial consortia to diseaseN AT U R E 5 3 5 : 9 4 , 2016 Rapid advances in DNA sequencing, metabolomics, proteomics andcomputational tools are dramatically increasing access to the microbiomeand identification of its links with
8、 disease. In particular, time-series studiesand multiple molecular perspectives are facilitating microbiome-wideassociation studies, which are analogous to genome-wide association studies.Early findings point to actionable outcomes of microbiome-wide associationstudies, although their clinical appli
9、cation has yet to be approved. An appreciation of the complexity of interactions among the microbiomeand the hosts diet, chemistry and health, as well as determining thefrequency of observations that are needed to capture and integrate thisdynamic interface, is paramount for developing precision dia
10、gnostics andtherapies that are based on the microbiome.Developing a microbialGlobal Positioning System tostratify individuals and toguide their treatmentRnomics snRNA snoRNA microRNA lncRNA Circular RNAOmics Genomics Epigenomics Transcriptomics Proteomics Metabolomics Interactomics PhenomicsMaximizi
11、ng the potential of the mouse as a model organismNature Review/Genetics 10:372 (2009)The biological organization of the -omicsGlobal views of the human interactomeMedical Data Clinical dataStructured medical recordMedical image Cohord research data Clinical research dataReal time physiological and p
12、athologicaldate from in situ sensors Wearable devices Film Chips Implantable chipsFully integrated wearable sensor arrays(FISA )for multiplexed perspiration analysisN AT U R E 529: 5 0 9 (2016)Sensing a shift in health careScience Translational Medicine 7 (283), 283rv3,2015Epidermal ElectronicsScien
13、ce 333, 838 (2011)LifeWatch獲得遠(yuǎn)程患者可粘貼監(jiān)測設(shè)備FDA許可 瑞士遠(yuǎn)程心臟監(jiān)測公司LifeWatch獲得其LifeWatch VSP(生命體征貼片)的FDA許可,是遠(yuǎn)程患者監(jiān)測貼片。 LifeWatch尺寸小、易粘貼、穿戴舒適,與現(xiàn)在使用的為數(shù)眾多的有線設(shè)備相比,限制更少。VSP可用于醫(yī)院、救護(hù)車、護(hù)理和家庭環(huán)境。用一個(gè)舒適得多且非常便宜的方式替代多個(gè)硬件設(shè)備。同時(shí)設(shè)備還可定制參數(shù)、設(shè)定提醒,可配置數(shù)據(jù)并兼容EMR系統(tǒng)。 LifeWatch是一次性粘貼帶,包含檢測心電圖、心率、呼吸頻率、體溫、血氧飽和度和運(yùn)動(dòng)的傳感器。他還包含電池,可通過設(shè)備連續(xù)收集數(shù)據(jù)五到七天。
14、針對(duì)VSP系統(tǒng)的許可還包括無線連接血壓腕帶和用于處理及傳輸數(shù)據(jù)的安卓app。app還能監(jiān)測患者,同時(shí)在發(fā)生異常生命體征時(shí)發(fā)出警報(bào) 。Soft, stretchable, fully implantable chipsSoft, stretchable, fully implantable miniaturized chipsmHealth taking center stageScience Translational Medicine 7 (283), 283rv3, 2015Heterogeneous and non-traditional sources of big dataGenom
15、ics Proteomics Bioinformatics 14: 3141 (2016)Time to Integrate Clinical andResearch InformaticsScience Translational Medicine 2012 Vol 4 Issue 162 162fs41Integration of clinical and research informatics can streamlineclinical research, patient care, and the building of a learninghealth care system.E
16、HRs are dominated by unstructured narrative data that are notavailable for research or quality improvement efforts.Clinical research databases which contain well-defined andstructured dataare created independently, and clinicallyrelevant data within these research databases are not availablefor purp
17、oses of clinical care.A meaningfully integrated approach to clinical and research datainformatics is needed to promote improved health outcomesand more rational allocation of health care resources.The Electronic Medical Records andGenomics (eMERGE) NetworkGenetics in medicine 15: 761(2013) The Elect
18、ronic Medical Records and Genomics Network is aNational Human Genome Research Institutefundedconsortium engaged in the development of methods and bestpractices for using the electronic medical record as a tool forgenomic research. The network has played a major role in validating the conceptthat cli
19、nical data derived from electronic medical records can beused successfully for genomic research. Current work is advancing knowledge in multiple disciplines atthe intersection of genomics and healthcare informatics,particularly for electronic phenotyping, genomewide associationstudies, genomic medic
20、ine implementation, and the ethical andregulatory issues associated with genomics research andreturning results to study participants.A vision and a prescription forbig dataenabled medicineNATURE IMMUNOLOGY 16: 435 (2015) Genetic, environmental and socioeconomicfactors render humanity remarkably div
21、erse.-Omic and sensor technologies permit thecapture of this diversity with unprecedentedprecision. Leveraging these technologies in clinical decisionmaking will help to bring about the long-heraldedpersonalization of medicine.Big dataenabled medicineNATURE IMMUNOLOGY 16: 435 (2015)From Big Data to
22、Intelligent Medicine Data qualification and digitization Data annotation and knowledge formation Simulation and mathematic modeling Precise and smart actionFrom Big Data to Intelligent Medicine Data qualification and digitization Data annotation and knowledge formation Simulation and mathematic mode
23、ling Precise and smart actionKnowledge management system A knowledge management system is defined as atool that selectively provides information relevantto a specific to the characteristics or circumstancesof a clinical situation but which requires humaninterpretation for direct application patient.
24、 Electronic KMSs include information retrievaltools and knowledge resources that consist ofdistilled primary literature on evidence-basedpracticesClinical decision support system A clinical decision support system is defined asany electronic system designed to aid directly inclinical decisionmaking,
25、 in which characteristics ofindividual patients are used to generate patient-specific assessments or recommendations that arethen presented to clinicians for consideration. Electronic CDSSs include alerts, reminders, ordersets, drug-dosage calculations, and care-summarydashboards that provide perfor
26、mance feedback onquality indicators or benchmarks.From Big Data to Intelligent Medicine Data qualification and digitization Data annotation and knowledge formation Simulation and mathematic modeling Precise and smart actionDigital liver anddigital manFederal regulators hopecomputer modeling can iden
27、tifydrugs that damage the liverbefore they do harmDRUG-INDUCED LIVER INJURY is theleading cause of acute liver failureand thesingle greatest reason the U.S. Food and DrugAdministration refuses to approve a drug orpulls a drug from market. The FDA seems toagree on the potential to build predictivecom
28、puter models which could identify livertoxicity problems early in the drugdevelopment process.Clinical decision support system A clinical decision support system is defined asany electronic system designed to aid directly inclinical decisionmaking, in which characteristics ofindividual patients are
29、used to generate patient-specific assessments or recommendations that arethen presented to clinicians for consideration. Electronic CDSSs include alerts, reminders, ordersets, drug-dosage calculations, and care-summarydashboards that provide performance feedback onquality indicators or benchmarks.Me
30、tabolomics in drug discoveryand precision medicineNATURE REVIEWS DRUG DISCOVERY 15 :473 , 2016 Metabolomics is an emerging omics science involving thecomprehensive characterization of metabolites and metabolism inbiological systems. Recent advances in metabolomics technologies areleading to a growin
31、g number of mainstream biomedical applications.In particular, metabolomics is increasingly being used to diagnosedisease, understand disease mechanisms, identify novel drug targets,customize drug treatments and monitor therapeutic outcomes. ThisReview discusses some of the latest technological advan
32、ces inmetabolomics, focusing on the application of metabolomics towardsuncovering the underlying causes of complex diseases (such asatherosclerosis, cancer and diabetes), the growing role of metabolomicsin drug discovery and its potential effect on precision medicine.Metabolites play a central part
33、in disease developmentA decision tree for metabolite-based drug discoveryand developmentiKnife Mass spectrometry-linked intelligent surgicaldevices iKnife can distinguish cancer and normal tissue in2 seconds by analyzing the smell.NATURE REVIEWS GASTROENTEROLOGY & HEPATOLOGY 12 :459,2015iKnifeiPad幫助
34、外科醫(yī)生更好的完成肝臟手術(shù) 德國Bremen的一位外科醫(yī)生在一臺(tái)iPad和虛擬現(xiàn)實(shí)軟件的幫助下,完成了一次肝臟手術(shù)。iPad的攝像頭將實(shí)時(shí)的肝臟拍下來,并且會(huì)層疊在虛擬的3D模型上。通過iPad查看肝臟中各種構(gòu)造,這樣可以更好的完成手術(shù)並計(jì)算肝臟部分是否能提供充足的供血,評(píng)估剩下的器官能否維持患者的生命。智能醫(yī)療系統(tǒng) 電腦醫(yī)生 智能醫(yī)院 智能醫(yī)聯(lián)網(wǎng)IBM Watson IBM Watsons cognitive computing capabilities has beengrowing throughout 2014, following the publicly disclosedlaun
35、ch of its Watson Genomics project and a relationshipwith the New York Genome Center. More recently, it haspublicized collaborations with the Cleveland Clinic and theMayo Clinic. The Watson Genomics project aims to significantly shortenthe week or more it currently takes bioinformaticians toexamine a
36、 patients unique molecular profile and identifydrugs for his or her individual treatment.IBM Watson answers questions posed in natural language, processesBig Data to uncover new patterns and learns from each interaction.IBM Wants to Load Doctor Watson toYour Smartphone IBM has invested into a geneti
37、c testing serviceslaboratory in a bid to give Watson, its self-learningcomputing system that can be controlled byhuman voice, the ability to answer questions a usermay have about their personal health. The investment makes Pathway Genomics Corp.one of the best capitalized healthcare startups,with $8
38、0 million raised total, according to IBM.智能醫(yī)療系統(tǒng) 電腦醫(yī)生 智能醫(yī)院 智能醫(yī)聯(lián)網(wǎng)More data-driven digital hospitalsEden Estopace | 2015-01-02 Against the backdrop of a global economy that is stillstruggling, healthcare cost is rising anywhere in the world,while demand for better health outcomes is also increasing. Re
39、search firm IDC predicts that operational efficiency inthe healthcare system will become so critical that by 2016,25 percent of hospitals will be budgeting for a data-drivendigital hospital strategy. “As we know, hospitals are in the middle of a profoundtransformation and across the globe we can see a lot ofmodernization initiatives that are aimed at accommodatingthe evolving role of hospitals in the health system,” saidSilvia Piai, Research Manager at IDC,
溫馨提示
- 1. 本站所有資源如無特殊說明,都需要本地電腦安裝OFFICE2007和PDF閱讀器。圖紙軟件為CAD,CAXA,PROE,UG,SolidWorks等.壓縮文件請(qǐng)下載最新的WinRAR軟件解壓。
- 2. 本站的文檔不包含任何第三方提供的附件圖紙等,如果需要附件,請(qǐng)聯(lián)系上傳者。文件的所有權(quán)益歸上傳用戶所有。
- 3. 本站RAR壓縮包中若帶圖紙,網(wǎng)頁內(nèi)容里面會(huì)有圖紙預(yù)覽,若沒有圖紙預(yù)覽就沒有圖紙。
- 4. 未經(jīng)權(quán)益所有人同意不得將文件中的內(nèi)容挪作商業(yè)或盈利用途。
- 5. 人人文庫網(wǎng)僅提供信息存儲(chǔ)空間,僅對(duì)用戶上傳內(nèi)容的表現(xiàn)方式做保護(hù)處理,對(duì)用戶上傳分享的文檔內(nèi)容本身不做任何修改或編輯,并不能對(duì)任何下載內(nèi)容負(fù)責(zé)。
- 6. 下載文件中如有侵權(quán)或不適當(dāng)內(nèi)容,請(qǐng)與我們聯(lián)系,我們立即糾正。
- 7. 本站不保證下載資源的準(zhǔn)確性、安全性和完整性, 同時(shí)也不承擔(dān)用戶因使用這些下載資源對(duì)自己和他人造成任何形式的傷害或損失。
最新文檔
- 2025年度房產(chǎn)抵押小微企業(yè)貸款合同模板
- 2025年度兒童房安全木門定制合同
- 2025年度專利技術(shù)許可協(xié)議模板-智能硬件
- 2025年度家具行業(yè)專利技術(shù)許可合同
- 冷藏肉類電商運(yùn)輸合同
- 2025年度導(dǎo)演聘用合同范例:院線電影導(dǎo)演合作協(xié)議書
- 2025年吉安職業(yè)技術(shù)學(xué)院單招職業(yè)傾向性測試題庫完整
- 2025年度農(nóng)業(yè)種植合同解除協(xié)議樣本
- 親子教育居間合同
- 2025年度文化旅游產(chǎn)業(yè)投資合作協(xié)議書范文
- 英語-廣東省上進(jìn)聯(lián)考領(lǐng)航高中聯(lián)盟2025屆高三下學(xué)期開學(xué)考試題和答案
- 2025年春季新北師大版生物七年級(jí)下冊(cè)全冊(cè)教學(xué)課件
- 培訓(xùn)課件:律師客戶溝通技巧
- 2025年春新外研版(三起)英語三年級(jí)下冊(cè)課件 Unit5第1課時(shí)Startup
- 2025年春新外研版(三起)英語三年級(jí)下冊(cè)課件 Unit1第2課時(shí)Speedup
- 生物新教材培訓(xùn)的心得體會(huì)
- 2024年07月長沙農(nóng)村商業(yè)銀行股份有限公司2024年招考3名信息科技專業(yè)人才筆試歷年參考題庫附帶答案詳解
- 中醫(yī)預(yù)防流感知識(shí)講座
- 上海市2024年中考英語試題及答案
- 臨床患者體位管理
- 砂光機(jī)培訓(xùn)課件
評(píng)論
0/150
提交評(píng)論