PONE-D-21-17467

A Deep Learning System for Heart Failure Mortality Prediction

PLOS ONE

Dear Dr. Li,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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Le Hoang Son, Ph.D

Academic Editor

PLOS ONE

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The article is supported by the following projects. (1) National Major Scientific The General Object of National Natural Science Foundation (62076177、61772358). (2) National Major Scientific Research Instrument Development Project (6202780085). (3) Shanxi Province key technology and generic technology R&D project (2020XXX007).

  

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Comments to the Author

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

Reviewer #1: Partly

Reviewer #2: Partly

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

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

Reviewer #2: No

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4. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #2: No

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5. Review Comments to the Author

Reviewer #1: 

In the present study, the authors presented a heart failure mortality prediction model using machine learning, which inspired many researchers. There are many models that predict the prognosis of heart failure by conventional statistical methods and currently, new predicing models using deep-learning algorithms have been introduced showing outstanding performance. In this study, the authors proposed an indicator vector to indicate whether the value is true or be padded, which fast solves the missing values and helps expand date dimensions. It appears that the study has been carefully done and the manuscript is well written and clearly presented. However, the following issues require further consideration and clarification.

First, authors should describe the development of machine learning models in detail enough for readers to reproduce the experiment.

As a major pitfall of machine learning (ML) algorithms is overfitting, external validation is needed. Without external validation, the study result is not guaranteed in other hospitals.

In order to understand the results more accurately, it is better to provide PPV, NPV of the model.

This study did not present the left ventricular function of the study population. In the study of heart failure, absence of LV ejection fraction data is a critical limitation to claim clinical value of the study. At this point, this study seems more suitable for a computer science journal than a medical journal.

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Reviewer #2: The authors should be commended for their interesting study

However, I have some questions that I feel should be addressed

1) The introduction is too long - it could be made significantly shorter by focussing on the role of ML mortality prediction in HF

2) I was confused about how the patient population is split - it wasn't clear if subjects could be a more than 1 group - please clarify maybe with a flow diagram

3) The feature vectorisation needs more explanation - I think representative example of the feature vector (in supplemental information) would be very useful

4) There is no need to discuss CNN architecture more generally only the 1D version.

5) I was confused to how the 1x90 feature vector is created please clarify in relation to other earlier sections

6) Using a 1D CNN results in kernels crossing very different features - please discuss - why not use an MLP

7) Figure 3 seems to switch between showing inputs to kernels - maybe these should be more obviously differentiated

8) The effect of the indicator vector and self-attention head are in the discussion not the results please move to results

9) Depp SHAP is only really mentioned in the discussion - maybe more description in methods and results is required

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

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PONE-D-21-17467R1A Deep Learning System for Heart Failure Mortality PredictionPLOS ONE

Dear Dr. Li,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by May 26 2022 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

• A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
• A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
• An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Le Hoang Son, Ph.D

Academic Editor

PLOS ONE

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Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #2: (No Response)

Reviewer #3: All comments have been addressed

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

Reviewer #2: Partly

Reviewer #3: Yes

**********

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

Reviewer #2: Yes

Reviewer #3: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #2: Yes

Reviewer #3: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #2: Yes

Reviewer #3: Yes

**********

6. Review Comments to the Author

Reviewer #2: 

I would like commend the authors the manuscript is much improved

1. However I am still concerned about the use of a CNN for the network - I agree that CNN are easier to train and it does leverage local connections - but that is not always a good thing. For instance does the order of vector features matter - what happens when convolutional kernels cross different groups. I would suggest doing a sensitivity analysis of different order of features

2. Also please put the comparison with different ML models in the results not discussion

3. The paper is still very long and needs to be edited to make it shorter

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Reviewer #3: Publish the revised manuscript as I am satisfied with the changes made by authors. I would like to appreciate the efforts made by authors in preparing the revised version.

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

A Deep Learning System for Heart Failure Mortality Prediction

PONE-D-21-17467R2

Dear Dr. Li,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Le Hoang Son, Ph.D

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #2: All comments have been addressed

**********

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

Reviewer #2: Yes

**********

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

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #2: All my comments have been addressed adequately and I have no further comments or questions about this manuscript