摘要
目的通过调查新生儿高胆红素血症发病高危因素,利用多种机器学习方法建立和评估新生儿高胆红素血症风险预测模型,为防治新生儿高胆红素血症研究提供依据。方法收集山东中医药大学附属医院儿科门诊及病房的新生儿,采用问卷调查方式收集新生儿高胆红素血症的数据,采用Python3中的Scikitlearn机器学习软件进行统计分析,运用Logistic回归算法进行筛选高胆红素血症相关指标。基于纵向体检队列资料抽样产生不同样本量的模拟数据,对数据运用6种机器学习算法建立新生儿高胆红素血症风险预测模型,采用受试者工作特征曲线下面积验证模型鉴别新生儿高胆红素血症的能力和准确性。结果在6种机器算法中,随机森林模型综合效果最佳,其风险预测模型重要性特征排名较高为孕期疾病、新生儿是否患有新生儿溶血病、出生后异常症状(感染)、是否为早产儿。结论基于机器学习下构建的新生儿高胆红素血症风险预测模型对于新生儿高胆红素血症的防治具有一定的临床诊断价值;根据其结果生成的新生儿黄疸管理系统可对高风险患儿加强管理及监测,减少或预防并发症的发生。
Objective To investigate high-risk factors for neonatal hyperbilirubinemia(NHB)and establish and evaluate a NHB risk prediction model using multiple machine learning methods,providing evidence for NHB prevention and treatment research.Methods The neonates from the pediatric outpatient and inpatient departments of Shandong University of Traditional Chinese Medicine Affiliated Hospital were enrolled.Data on neonatal hyperbilirubinemia were collected via questionnaire surveys.Statistical analysis was performed using the Scikitlearn machine learning software in Python3,with the Logistic regression algorithm employed to screen for hyperbilirubinemia-related indicators.Simulated data with varying sample sizes were generated from longitudinal physical examination cohort samples.Six machine learning algorithms were applied to establish neonatal hyperbilirubinemia risk prediction models.The ability and accuracy of these models to distinguish neonatal hyperbilirubinemia were validated using the area under the receiver operating characteristic curve(ROC).Results Among the six machine learning algorithms,the Random Forest model demonstrated the best overall performance.Key features ranking highly in this risk prediction model included pregnancy diseases,neonatal hemolytic disease,abnormal postnatal symptoms(infection),and preterm birth status.Conclusion The machine learning-based risk prediction model for neonatal hyperbilirubinemia holds clinical diagnostic value for its prevention and management.The resulting neonatal jaundice management system enables enhanced monitoring and management of high-risk infants,thereby reducing or preventing complications.
作者
丁雪
崔明明
周梦婕
娄溪萌
DING Xue;CUI Mingming;ZHOU Mengjie;LOU Ximeng(Shandong University of Traditional Chinese Medicine,Jinan 250061,China)
出处
《中国中西医结合儿科学》
2026年第1期12-17,F0003,共7页
Chinese Pediatrics of Integrated Traditional and Western Medicine
基金
2022年山东省医药卫生科技发展计划项目(鲁卫函[2022]467号)
山东省“齐鲁扁仓中医药人才”培育项目(鲁卫中医药科教字[2025]2号)
山东中医药大学附属医院“青年名中医”培养项目(省中人字[2024]60号)。
关键词
新生儿高胆红素血症
预测模型
机器学习
风险评估
Neonatal hyperbilirubinemia
Prediction model
Machine learning
Risk assessment
