科技英语摘要文章.docx

科技英语摘要文章.docx

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科技英语摘要文章

I.INTRODUCTION

THEbirthofbigdata,asaconceptifnotasaterm,isusually

associatedwithaMETAGroupreportbyDougLaney

entitled“3-DDataManagement:

ControllingDataVolume,

Velocity,andVariety”publishedin2001[1].Furtherdevelopments

nowsuggestbigdataproblemsareidentifiedbythe

so-called“5V”:

volume（quantityofdata）,variety（datafrom

differentcategories）,velocity（fastgenerationofnewdata）,veracity

（qualityofthedata）,andvalue（inthedata）[2].

Foralongtimethedevelopmentofbigdatatechnologieswas

inspiredbybusinessintelligence[3]andbybigscience（such

astheLargeHadronCollideratCERN）[4].Butwhenin2009

GoogleFlu,simplybyanalyzingGooglequeries,predictedflulike

illnessratesasaccuratelyastheCDC’senormouslycomplex

andexpensivemonitoringnetwork[5],someanalystsstartedto

claimthatallproblemsofmodernhealthcarecouldbesolved

bybigdata[6].

In2005,thetermvirtualphysiologicalhuman（VPH）was

introducedtoindicate“aframeworkofmethodsandtechnologies

that,onceestablished,willmakepossiblethecollaborative

ManuscriptreceivedSeptember29,2014;revisedDecember14,2014;accepted

February6,2015.DateofpublicationFebruary24,2015;dateofcurrent

versionJuly23,2015.

M.VicecontiiswiththeVPHInstituteforIntegrativeBiomedicalResearch,

andtheInsigneoInstituteforInSilicoMedicine,UniversityofSheffield,

SheffieldS13JD,U.K.（e-mail:

m.viceconti@sheffield.ac.uk）.

P.HunteriswiththeAucklandBioengineeringInstitute,Universityof

Auckland,1010Auckland,NewZealand（e-mail:

p.hunter@auckland.ac.nz）.

R.HoseiswiththeInsigneoinstituteforInSilicoMedicine,Universityof

Sheffield,SheffieldS13JD,U.K.（e-mail:

d.r.hose@sheffield.ac.uk）.

DigitalObjectIdentifier10.1109/JBHI.2015.2406883

investigationofthehumanbodyasasinglecomplexsystem”

[7],[8].Theideawasquitesimple:

1）Toreducethecomplexityoflivingorganisms,wedecompose

themintoparts（cells,tissues,organs,organsystems）

andinvestigateonepartinisolationfromtheothers.This

approachhasproduced,forexample,themedicalspecialties,

wherethenephrologistlooksonlyatyourkidneys,

andthedermatologistonlyatyourskin;thismakesitvery

difficulttocopewithmultiorganorsystemicdiseases,to

treatmultiplediseases（socommonintheageingpopulation）,

andingeneraltounravelsystemicemergencedue

togenotype-phenotypeinteractions.

2）Butifwecanrecomposewithcomputermodelsallthe

dataandalltheknowledgewehaveobtainedabouteach

part,wecanusesimulationstoinvestigatehowtheseparts

interactwithoneanother,acrossspaceandtimeandacross

organsystems.

Thoughthismaybeconceptuallysimple,theVPHvision

containsatremendouschallenge,namely,thedevelopmentof

mathematicalmodelscapableofaccuratelypredictingwhatwill

happentoabiologicalsystem.Totacklethishugechallenge,

multifacetedresearchisnecessary:

aroundmedicalimaging

andsensingtechnologies（toproducequantitativedataabout

thepatient’sanatomyandphysiology）[9]–[11],dataprocessing

toextractfromsuchdatainformationthatinsomecases

isnotimmediatelyavailable[12]–[14],biomedicalmodeling

tocapturetheavailableknowledgeintopredictivesimulations

[15],[16],andcomputationalscienceandengineeringtorun

hugehypermodels（orchestrationsofmultiplemodels）under

theoperationalconditionsimposedbyclinicalusage[17]–[19];

seealsothespecialissueentirelydedicatedtomultiscale

modeling[20].

Buttherealchallengeistheproductionofthatmechanistic

knowledge,quantitative,anddefinedoverspace,timeand

acrossmultiplespace-timescales,capableofbeingpredictive

withsufficientaccuracy.Aftertenyearsofresearchthishasproduced

acompleximpactscenarioinwhichanumberoftarget

applications,wheresuchknowledgewasalreadyavailable,are

nowbeingtestedclinically;someexamplesofVPHapplications

thatreachedtheclinicalassessmentstageare:

1）TheVPHOPconsortiumdevelopedamultiscalemodeling

technologybasedonconventionaldiagnosticimaging

methodsthatmakesitpossible,inaclinicalsetting,to

predictforeachpatientthestrengthoftheirbones,how

thisstrengthislikelytochangeovertime,andtheprobability

thattheywilloverloadtheirbonesduringdailylife.

Withthesethreepredictions,theevaluationoftheabsolute

riskofbonefractureinpatientsaffectedbyosteoporosis

willbemuchmoreaccuratethananypredictionbasedon

externalandindirectdeterminants,asitisincurrentclinical

practice[21].

2）Morethan500000end-stagerenaldiseasepatientsin

Europeliveonchronicintermittenthaemodialysistreatment.

Asuccessfultreatmentcriticallydependsonawellfunctioning

vascularaccess,asurgicallycreatedarteriovenous

shuntusedtoconnectthepatientcirculationto

theartificialkidney.TheARCHprojectaimedtoimprove

theoutcomeofvascularaccesscreationandlong-term

functionwithanimage-based,patient-specificcomputational

modelingapproach.ARCHdevelopedpatientspecific

computationalmodelsforvascularsurgerythat

makespossibletoplansuchsurgeryinadvanceonthe

basisofthepatient’sdata,andobtainapredictionofthe

vascularaccessfunctionoutcome,allowinganoptimization

ofthesurgicalprocedureandareductionofassociated

complicationssuchasnonmaturation.Aprospectivestudy

iscurrentlyrunning,coordinatedbytheMarioNegriInstitute

inItaly.Preliminaryresultson63patientsconfirm

theefficacyofthistechnology[22].

3）Percutaneouscoronaryintervention（PCI）guidedbyfractional

flowreserve（FFR）issuperiortostandardassessment

alonetotreatcoronariesstenosis.FFR-guidedPCI

resultsinimprovedclinicaloutcomes,areductioninthe

numberofstentsimplanted,andreducedcost.However,

currentlyFFRisusedinfewpatients,becauseitisinvasive

anditrequiresspecialinstrumentation.Alessinvasive

FFRwouldbeavaluabletool.TheVirtuHeartproject

developedapatient-specificcomputermodelthataccurately

predictsmyocardialFFRfromangiographicimages

alone,inpatientswithcoronaryarterydisease.Ina

phase1studythemethodsshowedanaccuracyof97%,

whencomparedtostandardFFR[23].Asimilarapproach,

butbasedoncomputedtomographyimaging,isevenata

moreadvancedstage,havingrecentlycompletedaphase2

trial[24].

WhiletheseandsomeotherVPHprojectshavereachedthe

clinicalassessmentstage,quiteafewotherprojectsarestillin

thetechnologicaldevelopment,orpreclinicalassessmentphase.

Butinsomecasesthemechanisticknowledgecurrentlyavailable

simplyturnedouttobeinsufficienttodevelopclinicallyrelevant

models.

Soitisperhapsnotsurprisingthatrecently,especiallyinthe

areaofpersonalizedhealthcare（sopromisingbutsochallenging）

somepeoplehavestartedtoadvocatetheuseofbigdata

technologiesasanalternativeapproach,inordertoreducethe

complexitythatdevelopingareliable,quantitativemechanistic

knowledgeinvolves.

Thistrendisfascinatingfromanepistemologicalpointof

view.TheVPHwasbornaroundtheneedtoovercomethe

limitationsofabiologyfoundedonthecollectionofahuge

amountofobservationaldata,frequentlyaffectedbyconsiderable

noise,andboxedintoaradicalreductionismthatprevented

mostresearchersfromlookingatanythingbiggerthanasingle

cell[25],[26].Suggestingthatwereverttoaphenomenological

approachwhereapredictivemodelissupposedtoemergenot

frommechanistictheoriesbutbyonlydoinghigh-dimensional

bigdataanalysis,maybeperceivedbysomeasasteptoward

thatempiricismtheVPHwascreatedtoovercome.

Inthefollowing,wewillexplainwhytheuseofbigdatamethods

andtechnologiescouldactuallyempowerandstrengthen

currentVPHapproaches,increasingconsiderablyitschancesof

clinicalimpactinmany“difficult”targets.Butinorderforthat

tohappen,itisimportantthatbigdataresearchersareawarethat

whenusedinthecontextofcomputationalbiomedicine,bigdata

methodsneedtocopewithanumberofhurdlesthatarespecific

tothedomain.Onlybydevelopingaresearchagendaforbig

dataincomputationalbiomedicinecanwehopetoachievethis

ambitiousgoal.

II.DOCTORSANDENGINEERS:

JOINEDATTHEHIP

Asengineerswhohaveworkedformanyyearsinresearch

hospitals,werecognizethatclinicalandengineeringresearchers

shareasimilarmind-set.Bothintraditionalengineeringandin

medicine,theresearchdomainisdefinedintermsofproblem

solving,notofknowledgediscovery.Themottocommontoboth

disciplinesis“whateverworks.”

Butthereisafundamentaldifference:

Engineersusuallydeal

withproblemsrelatedtophenomenaonwhichthereisalarge

bodyofreliableknowledgefromphysicsandchemistry.When

agoodreliablemechanistictheoryisnotavailable,engineers

resorttoempiricalmodels,asfarastheycansolvetheproblem

athand.Butwhentheydothis,theyareleftwithasenseof

fragilityandmistrust,andtheytrytoreplacethemassoonas

possiblewiththeory-basedmechanisticmodels,whichareboth

predictiveandexplanatory.

Medicalresearchersdealwithproblemsforwhichthereis

amuchlesswell-establishedbodyofknowledge;inaddition,

thisknowledgeisfrequentlyqualitativeorsemiquantitative,and

obtainedinhighlycontrolledexperimentsquiteremovedfrom

clinicalreality,inordertotamethecomplexityinvolved.Thus,

notsurprisingly,manyclinicalresearchersconsidermechanistic

models“toosimpletobetrusted,”andingeneralthewholeidea

ofamechanisticmodel