PONE-D-22-13428Explaining predictive factors in patient pathways using autoencodersPLOS ONE

Dear Dr. De Oliveira,

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.

==============================

ACADEMIC EDITOR: Please consider and address the comments from the reviewers very carefully. In addition to the technical content, language revision is also required.==============================

Please submit your revised manuscript by Aug 20 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,

Nattapol Aunsri, Ph.D.

Academic Editor

PLOS ONE

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at 

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and 

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2. We note that you have stated that you will provide repository information for your data at acceptance. Should your manuscript be accepted for publication, we will hold it until you provide the relevant accession numbers or DOIs necessary to access your data. If you wish to make changes to your Data Availability statement, please describe these changes in your cover letter and we will update your Data Availability statement to reflect the information you provide.

[Note: HTML markup is below. Please do not edit.]

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

**********

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?

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 #1: No

Reviewer #2: Yes

**********

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

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: In this article titled “Explaining predictive factors in patient pathways using autoencoders”, the authors investigated the performance of autoencoders to explain predictive factors in patient pathways. To develop the above, the authors develop a method to transform and represent complex medical event logs. The developed autoencoders have been compared with other deep learning and machine learning methods by testing on retrospective data from the SNIIRAM database, and demonstrates competitive prediction performances. The use of autoencoders for explaining predictive factors in patient pathways is an interesting application. However, the document can be improved and made clearer to the audience. The grammar of the manuscript needs to be improved and the methodology-result section needs some rearrangement. In addition, discussion on certain aspects of the results is lacking.

General issues/questions

1) Section 3 – line 202 (below eqn 8): The meaning of the sentence “ The explanation element…that the any input elements…” is not clear due to apparent mistake in sentence structure. Consider rewriting.

2) Section 5.2.1 – line 322: The acronym AUC-ROC was not defined, but was only defined in a later Section 5.2.3 (line 360).

3) Section 5.2.2: The authors state the selection of activation functions, layer dimension and loss function. Any particular reason why these hyperparameters were chosen? Was there any analysis/ hyperparameter tuning being made to optimise these hyperparameters, particularly because they are the same for all five DL models?

4) Section 5.2.3 – line 348: acronym ELBO is not defined

5) Section 5.2.3 - line 365: “DT, RF and LR…. [43]. Deep learning… [44]”. These sentences are general statements (not specific to the autoencoder section). It is suggested that a separate section (5.2.4) be added to include these statements as to provide clarity and avoid confusion to the readers. The last sentence of Section 5.2.3 “The experiments…. Windows 10 OS” could also be shifted to this newly added section.

6) Section 5.3 and Fig 5: The authors present quantitative results, including values for the AUC-ROC, AUC-PR, and MCC. The reviewer suggests that the authors give a simple explanation of these results, particularly what these values represent in relation to the case study presented? Also, any particular reason of the discrepancy in results between AE(J_I) and AE(J_F)? This should also be included in the discussion.

7) Section 5.4: The authors use “(a)” and “(b)” to reference to Fig 6. It is suggested that the authors use “Fig 6 (a)” and “Fig 6 (b)” as to improve clarity and avoid confusion. furthermore, “(c)” is mentioned in line 414, however such figure does not exist in Fig. 6. The reviewer believes this refers to Fig 7 instead, please amend.

8) Section 5.4 – line 416,417: The authors mention that “the relative risks with a 95% confidence interval….are presented in (a) and (b), respectively”. Again, this should be in reference to Fig 8 but was not mentioned. Please amend.

9) Section 5.4: the authors presented a method of validating the assumptions made from the results of Fig 6a by computing the relative risks. The following text describing this methodology should be exclusive to the methods section, rather than the results/discussion.

10) Conclusion – line 456: The authors mentioned that better strategies can be used to improve prediction performance and explainability. Could the authors provide some possible examples/potential methods to achieve this? If not, this sentence remains highly speculative.

11) This study focuses exclusively on patient pathways to explain predictive factors, while omitting patient characteristics such as sex, age, and race. Would the inclusion of these characteristics in the study improve performance and/or uncover hidden patterns? How would the omission of these variables affect potential clinical applications? As the mortality or other patient outcomes could be affected by such factors , ie there is an inherent predisposition of the patient due to these factors.

Minor issues:

1) Section 3 – line 157: “…input data x and return a lower…”. Should be “returns”

2) Section 4 – line 221: “..the considered case study”. Missing fullstop.

3) Section 4 – line 225: “..all level of…”. Should be “all levels of”

4) Section 5.2.1 – line 322: “…, evaluation the mean…” should be “…and evaluating the mean…”

5) Section 5.2.2 – line 330: “The 4 other architecture replace…” should be “The 4 other architectures replace…”. Also, suggested to use spelling for numbers that are less than 10, ie “four” instead of “4”.

6) Section 5.2.3 – line 346: “The training process consists in…”. Should it be “consists of”?

7) Section 5.2.3 – line 354: “For DL ans AE…”. Typo of the word “and”

8) Section 5.3 – line 373 “…, were the proposed methods…” should be “…, where the proposed methods…”.’

9) Section 5.4 – line 431: “…that various level…” should be “…that various levels”

10) Section 5.4 – line 393: “…train data by computing…” should be “…training data by computing…”

11) Conclusion – line 460: “..of the time widow”. Typo of the word “window”

Reviewer #2: Explaining predictive factors in patient pathways using autoencoders

PONE-D-22-13428

The abstract:

After reading the whole paper, I found that the abstract lacks the jism of the work. The work done is not reflected in the abstract. I would advise to review same. I understand that there is a limitation on the number of words for the abstract- However, it lacks the main aspect of the work.

Introduction:

Provided the research gap and the challenges that need to be addressed.

The main contribution is to explain the causal factors and to devise a framework to model the system

Introduction is explicit and well- written

Literature review

Right information to understand the topic

Section 3:

Section 3 focuses on autoencoders- However, this section seems to be disjoint over the whole paper. The explanations with the formula are good. However, it would be better if this section was linked with the work conducted in this paper and not just a preliminary where a reader does not have the interest why this is being discussed here.

Section 5:

Presents the case study. From what is reported is that a satisfactory amount of data was captured from 18, 678 patients

The authors have used machine learning (ML), deep learning (DL) and autoencoders (AE) to select and represent data. There are not enough details regarding deep learning.

Explainability of the parameters provided- However, the authors have not compared it with other author’s works. I understand that it is different, since it shows the explainability part- However, there is a need to provide a discussion in relation with other work.

One of the contributions of this paper is to validate the predictive factors extracted through relative risks, widely used in bio-statistical analysis. Some missing discussions in relation to the results and other people’s work.

Otherwise, it is a good piece of work.

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

**********

[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.

Explaining predictive factors in patient pathways using autoencoders

PONE-D-22-13428R1

Dear Dr. De Oliveira,

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.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Nattapol Aunsri, Ph.D.

Academic Editor

PLOS ONE

Additional Editor Comments : -

Reviewers' comments:

Reviewer's Responses to Questions

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 #1: All comments have been addressed

**********

2. 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

**********

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

Reviewer #1: 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 #1: Yes

**********

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

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The authors carefully considered and answered the reviewers' comments and questions. Thank you for the excellent job! The revised paper is a significantly improved version of the original manuscript, it is worth publishing.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

**********