![](/images/graphics-bg.png)
Machine Learning Models of Acute Kidney Injury Prediction in Acute Pancreatitis Patients
Joint Authors
Gao, Lin
Qu, Cheng
Ke, Lu
Yu, Xian-qiang
Fang, Guo-quan
He, Jianing
Cao, Long-xiang
Tong, Zhi-hui
Li, Wei-qin
Wei, Mei
Source
Gastroenterology Research and Practice
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-09-29
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
Background.
Acute kidney injury (AKI) has long been recognized as a common and important complication of acute pancreatitis (AP).
In the study, machine learning (ML) techniques were used to establish predictive models for AKI in AP patients during hospitalization.
This is a retrospective review of prospectively collected data of AP patients admitted within one week after the onset of abdominal pain to our department from January 2014 to January 2019.
Eighty patients developed AKI after admission (AKI group) and 254 patients did not (non-AKI group) in the hospital.
With the provision of additional information such as demographic characteristics or laboratory data, support vector machine (SVM), random forest (RF), classification and regression tree (CART), and extreme gradient boosting (XGBoost) were used to build models of AKI prediction and compared to the predictive performance of the classic model using logistic regression (LR).
XGBoost performed best in predicting AKI with an AUC of 91.93% among the machine learning models.
The AUC of logistic regression analysis was 87.28%.
Present findings suggest that compared to the classical logistic regression model, machine learning models using features that can be easily obtained at admission had a better performance in predicting AKI in the AP patients.
American Psychological Association (APA)
Qu, Cheng& Gao, Lin& Yu, Xian-qiang& Wei, Mei& Fang, Guo-quan& He, Jianing…[et al.]. 2020. Machine Learning Models of Acute Kidney Injury Prediction in Acute Pancreatitis Patients. Gastroenterology Research and Practice،Vol. 2020, no. 2020, pp.1-8.
https://search.emarefa.net/detail/BIM-1166759
Modern Language Association (MLA)
Qu, Cheng…[et al.]. Machine Learning Models of Acute Kidney Injury Prediction in Acute Pancreatitis Patients. Gastroenterology Research and Practice No. 2020 (2020), pp.1-8.
https://search.emarefa.net/detail/BIM-1166759
American Medical Association (AMA)
Qu, Cheng& Gao, Lin& Yu, Xian-qiang& Wei, Mei& Fang, Guo-quan& He, Jianing…[et al.]. Machine Learning Models of Acute Kidney Injury Prediction in Acute Pancreatitis Patients. Gastroenterology Research and Practice. 2020. Vol. 2020, no. 2020, pp.1-8.
https://search.emarefa.net/detail/BIM-1166759
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1166759