机器人外科学杂志（中英文）2025，Vol.6Issue(4)：655-659,666,6.DOI:10.12180/j.issn.2096-7721.2025.04.025
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)
Prediction model based on machine learning for auditory and speech rehabilitation outcomes in children after cochlear implantation(with explanatory video)
摘要
Abstract
Objective:To explore the application of machine learning techniques in predicting auditory and speech rehabilitation outcomes for children after cochlear implantation.Methods:187 children who underwent cochlear implantation at Beijing Children's Hospital Affiliated to Capital Medical University from January 2012 to October 2024 were selected.Data from the parents'evaluation of aural/oral performance of children questionnaire and clinical indicators were collected at device activation and 1,3,6,12,24,and 36 months after activation.Machine learning algorithms(Support Vector Machine,Random Forest,and Artificial Neural Network)were used to construct prediction models,with feature selection methods identifying key factors influencing rehabilitation outcomes.Results:The accuracy of prediction models constructed by Artificial Neural Network,Random Forest,and Support Vector Machine were 74.91%,71.02%,and 68.20%,respectively.Feature selection revealed 7 significant predictors(P<0.05):usage time of CI,age at activation,gender,educational level of primary caregiver,residence location,cochlear implant laterality,and preoperative hearing aid use.Conclusion:Machine learning techniques can effectively predict auditory and speech rehabilitation outcomes in children after cochlear implantation,which provides a novel tool and theoretical support for precise clinical assessment and personalized intervention.关键词
人工耳蜗/机器学习/听觉言语/儿童Key words
Cochlear Implant/Machine Learning/Aural and Oral Performance/Children分类
临床医学引用本文复制引用
白杰,李颖,金欣,晏美棂,刘海红..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)[J].机器人外科学杂志（中英文）,2025,6(4):655-659,666,6.基金项目
国家重点研发计划项目(2023YFF1203504) （2023YFF1203504）
北京市自然科学基金(7232059) （7232059）
高层次公共卫生技术人才建设专项(2022-3-016)National Key R&D Plan Project of China(2023YFF1203504) （2022-3-016）
Natural Science Foundation of Beijing(7232059) （7232059）
High-level Public Health Technical Personnel Construction Project(2022-3-016) （2022-3-016）
