Erratum: Machine learning-based prediction of off-pump coronary artery bypass grafting-associated acute kidney injury
Erratum to: J Thorac Dis 2024;16:4535-42
In the July 30, 2024 issue of J Thorac Dis, the paper “Machine learning-based prediction of off-pump coronary artery bypass grafting-associated acute kidney injury” by Dr. Song et al. (1) was published with some errors.
Corrections are shown below:
(I) In the section of Results, the sentences “JenyTable 2 shows the performance of these models when intraoperative data were added to the baseline data; the AUC of the GBDT model was again the highest (AUC =0.861), followed by RF model (AUC =0.780), XGBoost model (AUC =0.764), SVM model (AUC =0.730), AdaBoost model (AUC =0.726), LR model (AUC =0.700), KNN model (AUC =0.598) and DT model (AUC =0.550). These data demonstrate that the addition of intraoperative time series data resulted in a considerable increase in AUC; in case of the GBDT model, AUC increased by 0.122.” should be corrected to “JenyTable 2 shows the performance of these models when intraoperative data were added to the baseline data; the AUC of the GBDT model was again the highest (AUC =0.835), followed by SVM model (AUC =0.737), LR model (AUC =0.709), XGBoost model (AUC =0.703), RF model (AUC =0.656), AdaBoost model (AUC =0.578), KNN model (AUC =0.573) and DT model (AUC =0.443). These data demonstrate that the addition of intraoperative time series data resulted in a considerable increase in AUC; in case of the GBDT model, AUC increased by 0.096.”
The corrected Table 2 appears below.
Table 2
| Machine learning model | AUC | Accuracy rate |
|---|---|---|
| LR | 0.709 | 0.786 |
| SVM | 0.737 | 0.829 |
| DT | 0.443 | 0.829 |
| RF | 0.656 | 0.936 |
| KNN | 0.573 | 0.936 |
| GBDT | 0.835 | 0.929 |
| AdaBoost | 0.578 | 0.907 |
| XGBoost | 0.703 | 0.943 |
AUC, area under the curve; LR, logistic regression; SVM, support vector machine; DT, decision tree; RF, random forest; KNN, k-nearest neighbor; GBDT, gradient-boosting decision tree; AdaBoost, adaptive boosting; XGBoost, eXtreme gradient boosting.
Accordingly, the results presented in the abstract section require modification. Specifically, the intraoperative datasets should be updated to an AUC of 0.835 and an accuracy of 0.929, instead of the previously reported AUC of 0.861 and accuracy of 0.936.
(II) In the section of “Discussion”, “LS-LSTM” should be corrected as “CONV-LSTM”.
(III) In the “Abstract” section and the “Statistical analysis” section, “a long short-term memory (LSTM) deep learning model” should be changed to “a long short-term memory (CONV-LSTM) deep learning model”.
The authors regret the errors and confirm they will not change the conclusions of the article.
Click here to view the updated version of the article.
Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.
References
- Song Y, Zhai W, Ma S, et al. Machine learning-based prediction of off-pump coronary artery bypass grafting-associated acute kidney injury. J Thorac Dis 2024;16:4535-42. [Crossref] [PubMed]
