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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
ISSN/eISSN1465-9921
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
DOI10.1186/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
被引频次:4[WOS]   [WOS记录]     [WOS相关记录]
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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