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Original Research

Open Access

XGBoost model predicts acute lung injury after acute pancreatitis

  • Weiwei Lu1,2,†
  • Xi Chen3,†
  • Wei Liu4,†
  • Wenjie Cai5
  • Shengliang Zhu1
  • Yunkun Wang6,*,
  • Xiaosu Wang1,*,

1Department of Gastroenterology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, 200437 Shanghai, China

2Department of General Practice, Xinhua Hospital, Shanghai Jiaotong University School of Medicine, 200092 Shanghai, China

3Department of Emergency and Critical Care Medicine, Changzheng Hospital, Naval Medical University, 200003 Shanghai, China

4Department of Emergency, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, 200092 Shanghai, China

5School of Health Science and Engineering, University of Shanghai for Science and Technology, 200093 Shanghai, China

6Department of Pediatric Neurosurgery, Xinhua Hospital, Shanghai Jiaotong University School of Medicine, 200092 Shanghai, China

DOI: 10.22514/sv.2023.087 Vol.19,Issue 5,September 2023 pp.206-212

Submitted: 09 October 2022 Accepted: 25 November 2022

Published: 08 September 2023

*Corresponding Author(s): Yunkun Wang E-mail:
*Corresponding Author(s): Xiaosu Wang E-mail:

† These authors contributed equally.


To develop an XGBoost model to predict the occurrence of acute lung injury (ALI) in patients with acute pancreatitis (AP). Using the case database of Xinhua Hospital affiliated to Shanghai Jiaotong University School of Medicine, 1231 cases suffering from AP were screened, and after 137 variables were identified, the clinical characteristics of the samples were statistically analyzed, and the data were randomly divided into a training set (75%) to build the XGBoost model and a test set (25%) for validation. Finally, the performance of the model was evaluated based on accuracy, specificity, sensitivity, and subject characteristics working characteristic curves. The model performance is also compared with that of three other commonly used machine learning algorithms (support vector machine (SVM), logistic regression, and random forest). The age and laboratory tests of patients with AP combined with ALI differed from those of patients without combined acute lung injury. The area under the receiver operating characteristic (ROC) curve of the test set after model evaluation was 0.9534, the specificity was 0.7333, and the sensitivity was 0.7857, with arterial partial pressure of oxygen, bile acid, aspartate transaminase, urea nitrogen, and arterial blood pH as its most important influencing factors. In this study, the XGBoost model has advantages compared with other three machine learning algorithms. The XGBoost model has potential in the application of predicting acute lung injury after acute pancreatitis.


Acute pancreatitis; Acute lung injury; XGBoost; Predictive model

Cite and Share

Weiwei Lu,Xi Chen,Wei Liu,Wenjie Cai,Shengliang Zhu,Yunkun Wang,Xiaosu Wang. XGBoost model predicts acute lung injury after acute pancreatitis. Signa Vitae. 2023. 19(5);206-212.


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