Comparison of the diagnostic effect on different machine learning predictive model of imipenem sensitivity of Klebsiella pneumoniae based on MALDI-TOF MS
10.3760/cma.j.cn114452-20230420-00204
- VernacularTitle:基于MALDI-TOF MS的不同机器学习模型对肺炎克雷伯菌亚胺培南药物敏感性预测的诊断效能比较
- Author:
Han CHENG
1
;
Gang LIU
;
Lixia ZHANG
;
Qin WANG
;
Yu ZHU
Author Information
1. 天津市环湖医院检验科，天津 300350
- Keywords:
Artificial intelligence;
Klebsiella pneumoniae;
Machine learning;
Imipenem
- From:
Chinese Journal of Laboratory Medicine
2023;46(6):612-617
- CountryChina
- Language:Chinese
-
Abstract:
Objective:Machine learning is not only an important branch of artificial intelligence, but also supporting technologies for bioinformatics analysis. In the presence work, four machine-learning-predictive model for the drug-sensitivity of Klebsiella pneumoniae to imipenem were established based on matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS) and the diagnostic effect of these methods was exmained. Methods:A retrospective study was performed and the data of MALDI-TOF-MS and imipenem sensitivity of a total number of 684 cases Klebsiella pneumoniae isolated from clinical specimens in the laboratory of microbiology department of Tianjin Haihe Hospital from 2019 January to 2020 December were collected. The mass spectrometry and imipenem sensitivity data of 70 cases identified as imipenem-sensitive and 70 resistant cases were simple randomly selected to establish the training set model; whereas 30 cases of sensitive and 30 cases of resistant cases were randomly selected to establish the test set model. Mass spectral peak data were subjected to Orthogonal Partial least squares Discriminant Analysis (OPLS-DA). The training set data model was established by machine learning least absolute shrinkage and selection operator (LASSO) algorithm, Logistic Regression (LR) algorithm, Support vector machines (SVM) algorithm, neural network (NN) algorithm. The area under the curve (AUC) and confusion matrix of training set and test set model were calculated and selected by Grid search and 10-fold Cross-validation respectively, the accuracy of the prediction model was verified by test set confusion matrix. Results:The R2Y and Q2 of OPLS-DA were 0.546 3 and 0.017 8. The AUC of the best training set and test set models were 1.000 0 and 0.858 1, 1.000 0 and 0.820 1, 0.940 8 and 0.756 1, 1.000 0 and 0.697 2 evaluated by LASSO, LR, SVM and NN model respectively. The accuracy of the model were 99% (69/70), 100% (70/70), 91% (64/70) and 100% (70/70) for prediction of drug resistance, 100% (70/70), 100% (70/70), 90% (63/70) and 100% (70/70) for drug sensitivity prediction, the correct rate were 99% (139/140), 100% (140/140), 91% (127/140) and 100% (140/140) in training set, the test set showed that the accuracy were 93% (28/30), 87% (26/30), 60% (18/30) and 60% (18/30) for prediction of drug resistance, 100% (30/30), 80% (24/30), 93% (28/30) and 67% (20/30) for drug sensitivity prediction, the correct rate were 97% (58/60), 83% (50/60), 77% (46/60) and 63% (38/60) by LASSO, LR, SVM and NN model respectively.Conclusion:The LASSO prediction model of Klebsiella pneumoniae sensitivity to imipenem established in this study has a high accuracy rate and has potential clinical decision support ability.
