Title: Explainable machine learning model for predicting acute pancreatitis mortality in the
intensive care unit
Version: 1 Date: 24 Dec 2024
Reviewer:  Oguz Koc
Reviewer's report:
The paper is well organized, and it is an interesting research area. I have the following comments:
1-The paper needs further grammar check.
2-The authors must state why sensitivity is set as 100% more clearly. Is it for the elimination of
unnecessary patients beforehand for assigning purposes?
3-There are more effective ML algorithms in the literature such as Catboost and LightGBM,
they can be added to the study to increase the novelty since there is already a study that has been
published in December very similar to this research question.
Li, X., Tian, Y., Li, S. et al. Interpretable prediction of 30-day mortality in patients with acute
pancreatitis based on machine learning and SHAP. BMC Med Inform Decis Mak 24, 328 (2024).
https://doi.org/10.1186/s12911-024-02741-7
I do not suggest you add this study to the reference part, but the novelty needs to be increased.
Title: Explainable machine learning model for predicting acute pancreatitis mortality in the
intensive care unit
Version: 1 Date: 24 Dec 2024
Reviewer:  Oguz Koc
Reviewer's report:
The paper is well organized, and it is an interesting research area. I have the following comments:
1-The paper needs further grammar check.
2-The authors must state why sensitivity is set as 100% more clearly. Is it for the elimination of
unnecessary patients beforehand for assigning purposes?
3-There are more effective ML algorithms in the literature such as Catboost and LightGBM,
they can be added to the study to increase the novelty since there is already a study that has been
published in December very similar to this research question.
Li, X., Tian, Y., Li, S. et al. Interpretable prediction of 30-day mortality in patients with acute
pancreatitis based on machine learning and SHAP. BMC Med Inform Decis Mak 24, 328 (2024).
https://doi.org/10.1186/s12911-024-02741-7
I do not suggest you add this study to the reference part, but the novelty needs to be increased.
Please review this paper.
user
Title: Explainable machine learning model for predicting acute pancreatitis mortality in the
intensive care unit
Version: 1 Date: 24 Dec 2024
Reviewer:  Oguz Koc
Reviewer's report:
The paper is well organized, and it is an interesting research area. I have the following comments:
1-The paper needs further grammar check.
2-The authors must state why sensitivity is set as 100% more clearly. Is it for the elimination of
unnecessary patients beforehand for assigning purposes?
3-There are more effective ML algorithms in the literature such as Catboost and LightGBM,
they can be added to the study to increase the novelty since there is already a study that has been
published in December very similar to this research question.
Li, X., Tian, Y., Li, S. et al. Interpretable prediction of 30-day mortality in patients with acute
pancreatitis based on machine learning and SHAP. BMC Med Inform Decis Mak 24, 328 (2024).
https://doi.org/10.1186/s12911-024-02741-7
I do not suggest you add this study to the reference part, but the novelty needs to be increased.
assistant