METHODS: The data utilized in this study were collected from neonatology departments of four tertiary-level hospitals in China. To address the issue of imbalanced data, oversampling algorithms synthetic minority over-sampling technique (SMOTE) was applied to improve the model.
RESULTS: Seven hundred sixty one clinical records were collected in our study. Following data pre-processing and feature selection, 5 of the 46 features were used to build models, including duration of invasive respiratory support (day), the severity of BPD, ventilator-associated pneumonia, pulmonary hemorrhage, and early-onset PH. Four machine learning models were applied to predictive learning, and after comprehensive selection a model was ultimately selected. The model achieved 93.8% sensitivity, 85.0% accuracy, and 0.933 AUC. A score of the logistic regression formula greater than 0 was identified as a warning sign of BPD-PH.
CONCLUSIONS: We comprehensively compared different machine learning models and ultimately obtained a good prognosis model which was sufficient to support pediatric clinicians to make early diagnosis and formulate a better treatment plan for pediatric patients with BPD-PH.
方法:本研究使用的数据来自中国四家三级医院的新生儿科。为了解决数据不平衡的问题,过采样算法采用合成少数过采样技术(SMOTE)对模型进行了改进。
结果:在我们的研究中收集了761条临床记录。在数据预处理和特征选择之后,46个特征中有5个用于构建模型,包括有创呼吸支持的持续时间(天),BPD的严重程度,呼吸机相关性肺炎,肺出血,和早发性PH。四种机器学习模型被应用于预测学习,经过综合选择,最终选择了一个模型。该模型实现了93.8%的灵敏度,准确率85.0%,和0.933AUC。逻辑回归公式的得分大于0被识别为BPD-PH的警告信号。
结论:我们综合比较了不同的机器学习模型,最终获得了良好的预后模型,足以支持儿科临床医生对BPD-PH患儿进行早期诊断和制定更好的治疗方案。