PDF(Pubmed)
Abstract：
UNASSIGNED: Early identification and intervention of neurodevelopmental impairment in preterm infants may significantly improve their outcomes. This study aimed to build a prediction model for short-term neurodevelopmental impairment in preterm infants using machine learning method.
UNASSIGNED: Preterm infants with gestational age < 32 weeks who were hospitalized in The Maternal and Child Health Hospital of Guangxi Zhuang Autonomous Region, and were followed-up to 18 months corrected age were included to build the prediction model. The training set and test set are divided according to 8:2 randomly by Microsoft Excel. We firstly established a logistic regression model to screen out the indicators that have a significant effect on predicting neurodevelopmental impairment. The normalized weights of each indicator were obtained by building a Support Vector Machine, in order to measure the importance of each predictor, then the dimension of the indicators was further reduced by principal component analysis methods. Both discrimination and calibration were assessed with a bootstrap of 505 resamples.
UNASSIGNED: In total, 387 eligible cases were collected, 78 were randomly selected for external validation. Multivariate logistic regression demonstrated that gestational age(p = 0.0004), extrauterine growth restriction (p = 0.0367), vaginal delivery (p = 0.0009), and hyperbilirubinemia (0.0015) were more important to predict the occurrence of neurodevelopmental impairment in preterm infants. The Support Vector Machine had an area under the curve of 0.9800 on the training set. The results of the model were exported based on 10-fold cross-validation. In addition, the area under the curve on the test set is 0.70. The external validation proves the reliability of the prediction model.
UNASSIGNED: A support vector machine based on perinatal factors was developed to predict the occurrence of neurodevelopmental impairment in preterm infants with gestational age < 32 weeks. The prediction model provides clinicians with an accurate and effective tool for the prevention and early intervention of neurodevelopmental impairment in this population.
UNASSIGNED: Preterm infants with gestational age < 32 weeks who were hospitalized in The Maternal and Child Health Hospital of Guangxi Zhuang Autonomous Region, and were followed-up to 18 months corrected age were included to build the prediction model. The training set and test set are divided according to 8:2 randomly by Microsoft Excel. We firstly established a logistic regression model to screen out the indicators that have a significant effect on predicting neurodevelopmental impairment. The normalized weights of each indicator were obtained by building a Support Vector Machine, in order to measure the importance of each predictor, then the dimension of the indicators was further reduced by principal component analysis methods. Both discrimination and calibration were assessed with a bootstrap of 505 resamples.
UNASSIGNED: In total, 387 eligible cases were collected, 78 were randomly selected for external validation. Multivariate logistic regression demonstrated that gestational age(p = 0.0004), extrauterine growth restriction (p = 0.0367), vaginal delivery (p = 0.0009), and hyperbilirubinemia (0.0015) were more important to predict the occurrence of neurodevelopmental impairment in preterm infants. The Support Vector Machine had an area under the curve of 0.9800 on the training set. The results of the model were exported based on 10-fold cross-validation. In addition, the area under the curve on the test set is 0.70. The external validation proves the reliability of the prediction model.
UNASSIGNED: A support vector machine based on perinatal factors was developed to predict the occurrence of neurodevelopmental impairment in preterm infants with gestational age < 32 weeks. The prediction model provides clinicians with an accurate and effective tool for the prevention and early intervention of neurodevelopmental impairment in this population.
摘要：
■早期识别和干预早产儿的神经发育障碍可能会显著改善其结局。本研究旨在利用机器学习方法建立早产儿短期神经发育障碍的预测模型。
■在广西壮族自治区妇幼保健院住院的胎龄<32周的早产儿,并随访至18个月校正年龄，建立预测模型。训练集和测试集由MicrosoftExcel按照8:2随机划分。我们首先建立Logistic回归模型，筛选出对预测神经发育障碍有显著影响的指标。通过构建支持向量机来获得各指标的归一化权重,为了衡量每个预测因子的重要性，然后用主成分分析方法进一步降低指标的维数。辨别和校准都用505个重新采样的引导进行评估。
■总共，收集了387例符合条件的病例，随机选择78个进行外部验证。多因素logistic回归分析显示胎龄(p=0.0004),宫外生长受限（p=0.0367），阴道分娩（p=0.0009），高胆红素血症（0.0015）对预测早产儿神经发育障碍的发生更为重要。支持向量机在训练集上的曲线下面积为0.9800。模型的结果基于10倍交叉验证导出。此外,测试集上的曲线下面积为0.70。外部验证证明了预测模型的可靠性。
■开发了一种基于围产期因素的支持向量机，用于预测胎龄<32周的早产儿神经发育障碍的发生。该预测模型为临床医生提供了一个准确有效的工具，用于预防和早期干预该人群的神经发育障碍。
■在广西壮族自治区妇幼保健院住院的胎龄<32周的早产儿,并随访至18个月校正年龄，建立预测模型。训练集和测试集由MicrosoftExcel按照8:2随机划分。我们首先建立Logistic回归模型，筛选出对预测神经发育障碍有显著影响的指标。通过构建支持向量机来获得各指标的归一化权重,为了衡量每个预测因子的重要性，然后用主成分分析方法进一步降低指标的维数。辨别和校准都用505个重新采样的引导进行评估。
■总共，收集了387例符合条件的病例，随机选择78个进行外部验证。多因素logistic回归分析显示胎龄(p=0.0004),宫外生长受限（p=0.0367），阴道分娩（p=0.0009），高胆红素血症（0.0015）对预测早产儿神经发育障碍的发生更为重要。支持向量机在训练集上的曲线下面积为0.9800。模型的结果基于10倍交叉验证导出。此外,测试集上的曲线下面积为0.70。外部验证证明了预测模型的可靠性。
■开发了一种基于围产期因素的支持向量机，用于预测胎龄<32周的早产儿神经发育障碍的发生。该预测模型为临床医生提供了一个准确有效的工具，用于预防和早期干预该人群的神经发育障碍。
