PONE-D-24-04075Using machine learning methods to predict all-cause somatic hospitalizations in adults: A systematic review.PLOS ONE

Dear Dr. Askar,

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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Tariq Jamal Siddiqi

Academic Editor

PLOS ONE

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The publication charges for this article have been funded by a grant from the publication fund of UiT The Arctic University of Norway.

  

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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

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

Reviewer #1: Yes

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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: Yes

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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

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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: 1. While the introduction mentions the shift towards ML techniques, providing a brief rationale for why ML is superior to traditional regression models and specific risk indexes would strengthen the argument. For example, it could be helpful to highlight ML’s capacity to manage complex, high-dimensional datasets and identify nonlinear relationships.

2. The introduction cites studies that highlight the superiority of ML techniques. Providing a brief summary or key findings from these reviews would place the current study in the perspective of the existing literature and help readers understand the progression of research in the field of AI/ML.

3. In lines 53-54, authors have mentioned that recent reviews have proposed a framework for ML model development. This point needs to elaborated further.

4. In the methods section, while the authors have mentioned the removal of duplicate studies, providing details on the methodology used for duplicate removal (e.g., software/tool used) would enhance transparency of methods.

5. The authors should consider providing a brief explanation of the PROBAST tool and its domains to help readers understand how bias and applicability were assessed.

6. Line 162: In population characteristics, the authors can include total number of patients, and other baseline characteristics from the included studies.

7. Line 306-310: This can be highlighted as a limitation of this study, and can be elaborated further. Moreover, future directions are required as to how future studies can particularly tackle this.

8. Line 328, the sentence lacks a necessary comma. To improve clarity, it should be as follows: "The most used feature groups were demographics, diagnoses, physiological measurements, and laboratory tests, respectively."

9. In the "datasets and features" section of the discussion, the authors made a noteworthy observation regarding the increasing use of Natural Language Processing (NLP) techniques in recent studies. This trend shows a promising avenue with regard to utilizing textual data in predicting hospitalizations. Therefore, it is suggested to further discuss the potential benefits and challenges associated with employing NLP techniques for extracting information from clinical text.

10. In lines 340-351, the authors noted that a few studies reported handling class imbalance in the dataset and that resampling techniques, especially under sampling were the most used approach. To enhance reader comprehension, it would be beneficial to succinctly define and explain these techniques for those who may not be familiar with the terminology, thus improving overall reader understanding.

11. Similarly, in section ‘Models’ performance comparisons’, the authors mentioned a key finding that tree-based boosting algorithms often outperformed other algorithms. I suggest that the authors briefly explain what these algorithms are, how they work, significance of these algorithms and why they outperformed others.

12. In lines 399-400, the authors state that” In Supplementary 1: S6, we summarize the problems facing EV and the generalization of ML models along with some suggested solutions.” It would enhance the significance of the discussion to incorporate a brief overview of these challenges and solutions.

13. In lines 417-418, the authors emphasize the pivotal role of open-source datasets like MIMIC-III [94] in boosting machine learning (ML) research within the healthcare domain. To underscore the significance of these datasets further, the authors could cite evidence or statistics demonstrating the widespread impact of these open-source datasets. This will support the need of leveraging open data for advancing ML applications in healthcare.

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Reviewer #1: No

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PONE-D-24-04075R1Using machine learning methods to predict all-cause somatic hospitalizations in adults: A systematic review.PLOS ONE

Dear Dr. Askar,

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 Jul 29 2024 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,

Tariq Jamal Siddiqi

Academic Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

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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 #2: All comments have been addressed

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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 #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?

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 #2: 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 #2: The authors have submitted a revised study on using machine learning methods to predict all-cause somatic hospitalizations in adults.

- the authors have extensively revised and addressed all the comments provided in the first round of revision

- this study can benefit with a shorter introduction, which will improve readability of this paper.

- authors should consider mentioning company and version name for the referencing software of Mendeley

- instead of S2 or S3, authors can write Supplementary Table 3 or Supplementary Figure 2 etc etc.

- in my opinion, full forms of abbreviations mentioned in the table should be provided in a footnote or within somewhere in the title of the table, and not in S2 sheet.

- the authors should submit a document with double spaced text to improve readability for the reviewers as well

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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 #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.

Using machine learning methods to predict all-cause somatic hospitalizations in adults: A systematic review.

PONE-D-24-04075R2

Dear Dr. Askar,

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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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,

Tariq Jamal Siddiqi

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments: