Machinelearningapproachtopredictacutekidneyinjuryafterliversurgery

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Weincludedinouranalysispreoperativedatasuchasdemographiccharacteristics,laboratoryfindings,comorbidities,andmedication,aswellasperioperativedatasuchasdurationofsurgery.Thebaselinecharacteristicsincludedage,gender,anddyslipidemia.Dataontumorcharacteristicssuchasalpha-fetoprotein(AFP)andtumorsizewerealsocollected.Laboratorymeasurementsincludedhemoglobin,serumcreatinine,andcholesterol.Perioperativevariablesincludedtheuseofbloodproductsandsurgeryduration.

Serumcreatininefluctuationswerecontinuouslymonitoredaftertheoperation,andwerecomparedwiththepreoperativebaselinevalues.OurresultsindicatethataLSA-AKIeventoccurredin296patients(12.1%)within7daysaftersurgery.TheincidenceofAKIinthetrainingsetandtestsetwas11.5%(198/1715)and13.3%(98/735),respectively.

Therearealsosomelimitationsinourstudy.First,thiswasasingle-centerretrospectivestudy.Duetotherelativelysmallsamplesizeandthelackofexternalvalidation,ourresultsmaynotbegeneralizable.Second,includingallvariablesintheprocessofdatacollectionisaverychallengingtask,andthereforesomepotentiallyrelevantfactorsmayhavebeenignored.Finally,mostoftheinputtedfeatureswereimplementedmanually.Wearestillworkingondevelopingareal-timeautomatedelectronichealthrecordalgorithmthatcouldcollectperioperativepatientinformationfromavarietyofdatasources.Withthesenewtechnologies,predictivemodelsbasedonmachinelearningmayhavethepotentialtochangeclinicalpractice.

Recently,machinelearninghasprovenhelpfulintheinterpretationofmedicalresultsandhaspotentialforhelpingguidediagnosisandtreatment,ultimatelyimprovingpatientoutcomes.

Machinelearningmethodstopredictacutekidneyinjury(AKI)eventsremainlargelyunexplored.

WeaimedtodeveloppredictionmodelsforAKIafterlivercancerresectionbasedonmachinelearningtechniques.

Atotalof2450patientswhohadundergoneprimaryhepatocellularcarcinomaresectionatChangzhengHospital,ShanghaiCity,China,fromJanuary1,2015toAugust31,2020werescreened.Patientswererandomlyassignedtothetrainingandthetestsetsataratioof7:3.Thetrainingsetwasusedformodeldevelopmentandoptimization,whilethetestsetwasusedformodelvalidationandevaluation.

AKIeventsoccurredin296patients(12.1%)aftersurgery.Amongtheoriginalmodelsbasedonmachinelearningtechniques,therandomforest(RF)algorithmhadoptimaldiscriminationwithanareaunderthecurvevalueof0.92,comparedto0.87forextremegradientboosting,0.90fordecisiontree,0.90forsupportvectormachine,and0.85forlogisticregression.TheRFalgorithmalsohadthehighestconcordance-index(0.86)andthelowestBrierscore(0.076).ThevariablesthatcontributedthemostintheRFalgorithmwereage,cholesterol,andsurgerytime.

MachinelearningtechnologycanaccuratelypredictAKIafterhepatectomy.

Intheeraofpersonalizedmedicine,ourmodelbasedonmachinelearningcandiscriminatepatientsathighriskforAKI,thushelpingguideclinicaldecisionsandfacilitatingprospectiveinterventionsforhigh-riskindividuals.

Provenanceandpeerreview:Unsolicitedarticle;Externallypeerreviewed.

Peer-reviewmodel:Singleblind

Specialtytype:Urologyandnephrology

Country/Territoryoforigin:China

Peer-reviewreport’sscientificqualityclassification

GradeA(Excellent):0

GradeB(Verygood):0

GradeC(Good):C

GradeD(Fair):0

GradeE(Poor):0

P-Reviewer:VeelkenRS-Editor:GongZML-Editor:WebsterJRP-Editor:GongZM

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学习与动机杂志LearningAndMotivation

国外在线课程评价研究现状及启示——基于WOS期刊文献（2000—2017）的引文空间分析

Machinelearningapproachtopredictacutekidneyinjuryafterliversurgery

LanguageLearningJournal审稿速度

《3DDeepLearningApproachforRemoteSensingImageClassification》论文笔记AllFever

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