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機(jī)器學(xué)習(xí)模型預(yù)測(cè)機(jī)器人輔助腎部分切除術(shù)后腎功能減退

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【中圖分類(lèi)號(hào)】R737.11 【文獻(xiàn)標(biāo)志碼】A

Application of machine learning models in predicting renal function decline following robot-assisted partial nephrectomy

Li Jing',Wang Linfeng2,Zhang Gaojie2,Huang Yong2,Gao Yingying3,Sun Rui’,Cao Yang',Li Qiuchen', He Hao2,Wei Ziling',Liu Jiayu2 (1. The First Clinical College of Chongqing Medical University;2. Department of Urology, The First Affiliated Hospital of Chongqing Medical University ;3. Department of Clinical Laboratory, Banan Hospital Afliated to Chongqing Medical University)

【Abstract]Objective:Tocomparetheeficacyofvarious machinelearning modelsinpredictingrenalfunctiondeclineafterobotassistedpartialnephrectoy(RAP)ndtoprovideevdencefoicaliskatfication.Methods:Thssdyetrosectielyincludedtheclinicaldataof73patientswithrnalcellcarcinomaundergongRAPNattheUrologyDepartmentofTheFirstAiliated Hospitalof ChongqingMedical UniversityfromJanuary2019 toDecember2023.Demographiccharacteristics,laboratoryindicators, andperioperativeparameters ereintegrated toconstructseven machinelearning models.Keypredictors wereinterpretedusing Shapleyaditiveexplanations(SHAP).Modelperformancewas evauatedusingtheareaunder thereceiveroperatingcharacteristiccurve (AUC).Results:Therandomforestmodel demonstrated thebestpredictiveperformance(AUC=0.84).SHAPanalysisidentified neutrophil-tolpytetimordmetereaialalizdatifprotobiniitebloodcelloutd traoperativebloodlossassignificantfactorsinfluencing postoperativerenalfunctiondeclie.Conclusion:Thisstudyprovidesapotentialpredictivetolforlnicalpractieidgindentifgigskpatintsndtiiingpotopeatimanagenei.

【KeyWordsrobotasistedpartialneprectomyenalfunctiodlin;macinelearnngmodel;Shapleydditiveexplanatiordic tive model; postoperative management

腎細(xì)胞癌(renalcellcarcinoma,RCC)是起源于腎實(shí)質(zhì)泌尿上皮系統(tǒng)的惡性腫瘤,占所有新發(fā)癌癥的 2 % ~ 3 % ,其主要發(fā)病群體為男性。(剩余9367字)

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