基于机器学习的人工耳蜗植入术后儿童听觉言语康复效果预测模型研究（附讲解视频

打开文本图片集

中图分类号 R764.5 文献标识码 A 文章编号 2096-7721（2025）04-0655-06

Prediction model based on machine learning for auditory and speech rehabilitation outcomes in children after cochlear implantation （with explanatory video）

BAI Jie，LI Ying， JIN Xin， YAN Meiling， LIU Haihong （DepartmentofOtolaryngogHeadandNeckSurgeryeingCdren’sHospital，CapitalMdicalUiversitNatioalCtefr Children'sHealth，Beijing10oo45，China）

AbstractObjective：To exploretheaplicationof machine learming techniques inpredictingauditoryand speech rehabilitation outcomesforchildrenaftercochlearimplantation.Methods：187childrenwhounderwentcochlearimplantationatBeijingChildren’s HospitalAfiliatedtoCapitalMedicalUniversityfromJanuaryO12toOctober2O24wereselectedDatafromheparents’evaluation ofauraloralpforaeofdenuestoirendcalincatosrecoltedatvicectiatiod1，，6d 36 monthsafteractivation.Machinelearningalgorithms （Support VectorMachine，RandomForest，andArtificialNeuralNetwork） wereused toconstructpredictionmodels，withfeatureselectionmethodsidentifyingkeyfactors influencingrehablitationoutcomes. Results：Theacuracyof predictionmodelsconstructedbyArtificialNeuralNetwork，RandomForest，and Support VectorMachine were 7 4 . 9 1 % 7 1 . 0 2 % ，and 6 8 . 2 0 % ，respectively.Feature selection revealed 7 significant predictors （ P <0.05）： usage time of CI， age at activation，ucaalveofayiidclaontlaalitydiid Conclusion：Machinelearing techniquescanefectivelypredictauditoryandspeechrehabilitationoutcomesinchildrenaftercochlear implantationhichprovidesanoveltolandtheoreticalsupportforpreciseclincalassessmentandpersonalzedinteetio. KeyWords Cochlear Implant； Machine Learning；Aural and Oral Performance； Children

世界卫生组织在《世界听力报告》中指出，全球超过15亿人存在一定程度的听力损失，其中至少4.3亿人需要专业的听力康复进行干预。（剩余8311字）