PONE-D-20-10776

Application of a time-series deep learning model to predict cardiac dysrhythmias in electronic health records

PLOS ONE

Dear Dr. Foraker,

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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We look forward to receiving your revised manuscript.

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Sreeram V. Ramagopalan

Academic Editor

PLOS ONE

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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: No

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

Reviewer #1: No

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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: No

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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: In their manuscript titled "Application of a time-series deep learning model to predict cardiac dysrhythmias in electronic health records", Guo et al use the Electronic healthcare records (EHR) data available in The Guideline Advantage (TGA) and deep learning methodologies to make predictions on cardiac dysrythimas (CD) based on cardiovascular health (CVH) measures. They propose that LSTM performs better than LR, RF and NB and identify blood pressure and BMI as the most influential features.

Extracting time series information based on EHR data is a topic of high interest in the community and the question the authors pose, namely "can we predict CD based on EHR data using machine learning methodologies?" is very timely because the quality and quantity of available data is increasing and machine learning techniques have become not only more powerful but also more interpretable and robust. The authors states that the paper is the first time that DL algorithms have been applied to predict CD using time series EHR data. However, the manuscript has two major shortcomings; the methodology is not described in detail to be reproducible and the time dependent information does not reflect real time.

Time dependence: DL has been used for different classification tasks on EHR and CD prediction using different types of datasets (Solares 2020, Xia 2018, Shashikumar 2017). This study builds on literature by applying existing methods to a previously studied dataset. The authors state that this is the first that DL is applied to predict CD using time series EHR data. Indeed, if time dependence is incorporated into the predictive model, this is very interesting. However, it is not clear how time is included in the representations. The authors also mention this in the Limitations section. The events are represented by 311 time steps. Are two events separated by two days treated the same way as two events separated by two months? The authors state they use padding to create events so that they can have a 311D vector. How is this padding performed? If dynamic data is the major contribution of the paper, more information about the treatment of time should be included. Furthermore, it is not clear how blood pressure and BMI are the most influential features are impacted by time dependence.

Word2Vec: The study first constructs categorical features to represent patients and then learns continuous vectors usingWord2Vec algorithm. The authors can compare Word2Vec with categorical representations to show the value added by distributed representations. Besides, the construction of categorical vectors and the association of vectors with time can be detailed further, since these are key to the representations.

LSTM: LSTM is adopted as a neural time-series model to identify CD patients and it is compared with three benchmark models: Naive Bayes, Logistic Regression and Random Forest. Given that these models are neither designed to classify time-series nor as complex as a neural network, other models, such as non-neural time-series models and feed-forward neural networks, can be added as benchmarks to reveal the benefits of LSTM further. This would also uncover if the performance gain is due to time-series representation or complexity of neural models.

Causation: The study concludes that “BMI and blood pressure could lower the chance of developing CD” based on the fact that the removal of these features affected the prediction performance the most. This association can be analyzed in more detail, since these features are first embedded using Word2Vec and, thus, not directly observed by the prediction model. Moreover, the causation can be shown explicitly, since the performance drop might not necessarily indicate a causation but might simply mean that these features helped model to discriminate CD patients from the healthy group the most.

DL model: The code is not yet available. The authors have not included important details about the algorithms used. For example, how do the authors test for overfit? Is 5-fold cross validation performed? What is the error in their performance metrics? What is the statistical significance of their model comparison metrics?

The manuscript is well organized and easy to follow but more details about the methodology are needed and the conclusions are not supported by the experiments.

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Reviewer #1: No

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PONE-D-20-10776R1

Application of a time-series deep learning model to predict cardiac dysrhythmias in electronic health records

PLOS ONE

Dear Dr. Foraker,

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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Dear Authors, you have since the acceptance of this manuscript, resubmitted the manuscript with changes stating that analyses were revised and different results obtained. On the version that I received, these changes were not reflected in the abstract and elsewhere in the manuscript. As these changes were materially impacting the manuscript we will send the manuscript for re-review. If you do wish to resubmit the manuscript please make sure the updated manuscript fully accounts for the new results, otherwise we will not be willing to proceed further with you manuscript.

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Please submit your revised manuscript by Nov 27 2020 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: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols

We look forward to receiving your revised manuscript.

Kind regards,

Sreeram V. Ramagopalan

Academic Editor

PLOS ONE

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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 #1: 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 #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 have addressed the concerns. The authors can include some references to time aware LSTM for future work (eg DOI: 10.1145/3097983.3097997).

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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 #1: 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.

Application of a time-series deep learning model to predict cardiac dysrhythmias in electronic health records

PONE-D-20-10776R2

Dear Dr. Foraker,

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.

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Kind regards,

Sreeram V. Ramagopalan

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments: