Abstract:
Background In recent years, with the development of artificial intelligence, various machine learning models have been initially applied in clinical practice.
Objective To study the factors associated with multidrug-resistant Klebsiella pneumoniae (MDR-KPN) infection in hospitals using traditional logistic regression and machine learning logistic regression (LR model), respectively, and compare the differences of the two methods.
Methods A total of 254 cases of hospital-acquired Klebsiella pneumoniae infection in a tertiary hospital were selected, including 168 cases of MDR-KPN and 86 cases of non-MDR-KPN. Two different logistic regression methods were used to process the data.
Results Multivariate logistic analysis based on IBM-SPSS showed that liver disease, history of puncture of chest cavity within 3 months, history of puncture of artery within 3 months, and endotracheal intubation were independent factors associated with MDR-KPN hospital-acquired infection. LR model showed that endotracheal intubation was the most important factor associated with nosoacquired MDR-KPN infection, and the top five factors were endotracheal intubation, liver disease, age of 18-49 years, carbapenems exposure history, and central venous catheterization history.
Conclusion The factors associated with MDR-KPN hospital-acquired infection obtained by the two methods have a certain degree of consistency. LR model has the advantages of high efficiency, convenience, and quantifiable accuracy, suggesting that it has a certain application prospect in clinical practice.
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