tegrating3Dmodelrepresentationforanaccuratenon-inva-siveassessmentofpressureinjurieswithdeeplearning[J].Sensors(Basel),2020,20(10):2933.[14]GARCÍA-ZAPIRAINB,ELMOGYM,EL-BAZA,etal.Classificationofpressureulcertissueswith3Dconvolutionalneuralnetwork[J].Medical&BiologicalEngineering&Computing,2018,56(12):2245-2258.[15]LID,MATHEWSC.Automatedmeasurementofpressurein-jurythroughimageprocessing[J].JournalofClinicalNurs-ing,2017,26(21/22):3564-3575.[16]BARTZ-KURYCKIMA,GREENC,ANDERSONKT,etal.Enhancedneonatalsurgicalsiteinfectionpredictionmodelutilizingstatisticallyandclinicallysignificantvariablesincombinationwithamachinelearningalgorithm[J].AmericanJournalofSurgery,2018,216(4):764-777.[17]MICHELSONJD,PARISEAUJS,PAHANELLIWC.As-sessingsurgicalsiteinfectionriskfactorsusingelectronicmedicalrecordsandtextmining[J].AmericanJournalofIn-fectionControl,2014,42(3):333-336.[18]SOLANKISL,PANDROWALAS,NAYAKA,etal.Artifi-cialintelligenceinperioperativemanagementofmajorgastro-intestinalsurgeries[J].WorldJournalofGastroenterology,2021,27(21):2758.[19]UMSCHEIDCA,MITCHELLMD,DOSHIJA,etal.Esti-matingtheproportionofhealthcare-associatedinfectionsthatarereasonablypreventableandtherelatedmortalityandcosts[J].InfectionControlandHospitalEpidemiology,2011,32(2):101-114.[20]TUNTHANATHIPT,SAE-HENGS,OEARSAKULT,etal.Machinelearningapplicationsforthepredictionofsurgicalsiteinfectioninneurologicaloperations[J].NeurosurgFocus,2019,47(2):e7.[21]HOPKINSBS,MAZMUDARA,DRISCOLLC,etal.Usingartificialintelligence(AI)topredictpostoperativesurgicalsiteinfection:aretrospectivecohortof4046posteriorspinalfusions[J].ClinicalNeurologyandNeurosurgery,2020,192:54.[22]PETROSYANY,THAVORNK,SMITHGetal.Predictingpostoperativesurgicalsiteinfectionwithadministrativedata:arandomforestsalgorithm[J].BMCMedicalResearchMeth-odology,2021,21(1):179.[23]SOHNS,LARSONDW,HABERMANNEB,etal.Detec-tionofclinicallyimportantcolorectalsurgicalsiteinfectionusingBayesiannetwork[J].JournalofSurgicalResearch,2017,209:168-173.[24]ALMAMLOOKRE,WELLSLJ,S...
