PDF(Pubmed)
Abstract：
BACKGROUND: Bronchopulmonary dysplasia-associated pulmonary hypertension (BPD-PH) remains a devastating clinical complication seriously affecting the therapeutic outcome of preterm infants. Hence, early prevention and timely diagnosis prior to pathological change is the key to reducing morbidity and improving prognosis. Our primary objective is to utilize machine learning techniques to build predictive models that could accurately identify BPD infants at risk of developing PH.
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.
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.
摘要：
背景：支气管肺发育不良相关性肺动脉高压（BPD-PH）仍然是严重影响早产儿治疗结果的严重临床并发症。因此，早期预防和病理改变前的及时诊断是降低发病率和改善预后的关键。我们的主要目标是利用机器学习技术来建立预测模型，以准确识别患有PH风险的BPD婴儿。
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。
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。
