PONE-D-24-28799A Machine Learning-Based Prediction of Hospital Mortality in Mechanically Ventilated ICU PatientsPLOS ONE

Dear Dr. Pishgar,

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Additional Editor Comments:

Dear authors,

We have assessed your manuscript and you can see that the reviewers are requesting a revised manuscript for a final decision.

Regards

Upaka Rathnayake

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: No

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Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

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Reviewer #1: This paper discusses an interesting approach to machine learning-based prediction for hospital mortality in mechanically ventilated ICU patients. This analysis is crucial in ICU facility management. Hence, I recommend this paper for minor revision, and the following are a few comments to improve the paper,  

This paper does not include a graphical abstract. It is recommended to include a graphical abstract.

What approach would you suggest comparing the mechanically ventilated ICU patients of Chronic heart failure with patients with liver failure?

How could you improve the accuracy of your predictive model? What are the limitations?

What outcomes would you expect in your validation studies?

Compare your model with similar models in literature and elaborate more in the discussion section.

Reviewer #2: Its a good study and reasonably well conducted: However, following concerns are there

1. The literature review is not thorough, need to improve a lot, there should be a flow of ideas clearly showing the gap

2. Why did the authors selected these models was not clear to me

3. Improve the clarity of the figures, present versions are default generated ones from python

4. Rather than suddenly jumping to SHAP, it is good to mention the applicability of SHAP in medical domain, for you references:

https://www.sciencedirect.com/science/article/pii/S2667305324000723

https://www.sciencedirect.com/science/article/pii/S2772442524000030

5. Future research should explore the integration of deep learning approaches, such as 269

Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), " these are mostly used for time series analysis. Are they applicable for type of tabular records you used ?

6. COnclusion needs a revision, the present one is not good. PLease summarise the main findings. Dont include limitations as you do have a seperate section for that

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Reviewer #1: No

Reviewer #2: No

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