发表状态 | 已发表Published |
题名 | A comprehensive study on machine learning models combining with oversampling for bronchopulmonary dysplasia-associated pulmonary hypertension in very preterm infants |
作者 | |
发表日期 | 2024-12-01 |
发表期刊 | Respiratory Research
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ISSN/eISSN | 1465-9921 |
卷号 | 25期号:1 |
摘要 | 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. |
关键词 | Bronchopulmonary dysplasia Machine learning Oversampling Prediction model Pulmonary hypertension |
DOI | 10.1186/s12931-024-02797-z |
URL | 查看来源 |
收录类别 | SCIE |
语种 | 英语English |
WOS研究方向 | Respiratory System |
WOS类目 | Respiratory System |
WOS记录号 | WOS:001216268800001 |
Scopus入藏号 | 2-s2.0-85192387348 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | https://repository.uic.edu.cn/handle/39GCC9TT/12075 |
专题 | 理工科技学院 |
通讯作者 | Li, Qiuping |
作者单位 | 1.Newborn Intensive Care Unit,Faculty of Pediatrics,the Seventh Medical Center of PLA General Hospital,Beiing,China 2.The Second School of Clinical Medicine,Southern Medical University,Guangzhou,China 3.School of Software,Tsinghua University,Beijing,China 4.Department of Cardiology,Hunan Children’s Hospital,Changsha,China 5.Department of Neonatology,Qingdao Women and Children’s Hospital,Qingdao,China 6.Department of Neonatology,Tianjin Central Hospital of Gynecology Obstetrics,Tianjin,China 7.Department of Neonatology,Guangdong Women and Children Hospital,Guangdong Neonatal ICU Medical Quality Control Center,Guangzhou,China 8.Pediatric and Congenital Cardiology,Taussig Heart Center,Johns Hopkins School of Medicine,Baltimore,United States 9.Department of Statistics and Data Science,BNU-HKBU United International College,Zhuhai,China 10.Department of Neonatology,Nanfang Hospital,Southern Medical University,Guangzhou,China |
推荐引用方式 GB/T 7714 | Wang, Dan,Huang, Shuwei,Cao, Jingkeet al. A comprehensive study on machine learning models combining with oversampling for bronchopulmonary dysplasia-associated pulmonary hypertension in very preterm infants[J]. Respiratory Research, 2024, 25(1). |
APA | Wang, Dan., Huang, Shuwei., Cao, Jingke., Feng, Zhichun., Jiang, Qiannan., .. & Li, Qiuping. (2024). A comprehensive study on machine learning models combining with oversampling for bronchopulmonary dysplasia-associated pulmonary hypertension in very preterm infants. Respiratory Research, 25(1). |
MLA | Wang, Dan,et al."A comprehensive study on machine learning models combining with oversampling for bronchopulmonary dysplasia-associated pulmonary hypertension in very preterm infants". Respiratory Research 25.1(2024). |
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