Machine learning for predicting antimicrobial resistance in critical and high-priority pathogens: A systematic review considering antimicrobial susceptibility tests in real-world healthcare settings

Carlos M. Ardila; Daniel González-Arroyave; Sergio Tobón

doi:10.1371/journal.pone.0319460

Peer Review History

Original SubmissionMay 13, 2024
13 May 2024 Author Response https://doi.org/10.1371/journal.pone.0319460.r001
13 Dec 2024 Decision Letter - Mohamed O Ahmed, Editor PONE-D-24-19315Machine learning for predicting antimicrobial resistance in critical and high-priority pathogens: A systematic review considering antimicrobial susceptibility tests in real-world healthcare settings.PLOS ONE Dear Dr. Ardila, 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 Jan 27 2025 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. 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The table must include the following information for each study: Name of data extractors and date of data extraction Confirmation that the study was eligible to be included in the review. All data extracted from each study for the reported systematic review and/or meta-analysis that would be needed to replicate your analyses. If data or supporting information were obtained from another source (e.g. correspondence with the author of the original research article), please provide the source of data and dates on which the data/information were obtained by your research group. If applicable for your analysis, a table showing the completed risk of bias and quality/certainty assessments for each study or outcome. Please ensure this is provided for each domain or parameter assessed. For example, if you used the Cochrane risk-of-bias tool for randomized trials, provide answers to each of the signalling questions for each study. If you used GRADE to assess certainty of evidence, provide judgements about each of the quality of evidence factor. This should be provided for each outcome. An explanation of how missing data were handled. This information can be included in the main text, supplementary information, or relevant data repository. Please note that providing these underlying data is a requirement for publication in this journal, and if these data are not provided your manuscript might be rejected. 4. 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. [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: Partly Reviewer #2: Yes Reviewer #3: Yes ******** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: N/A Reviewer #2: Yes Reviewer #3: 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 Reviewer #3: 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 Reviewer #3: 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: This is a well written report which provides a valuable contribution to avenues relevant to the exploration of AMR and AMS. Some sections require further clarification to provide context to the approach undertaken. Reviewer #2: 1. This manuscript heavily discuses on AUROC. It would be helpful if the authors provided a figure that summarizes AUROC, and their conclusions based on the various articles they studied. 2. Digital methods, such as machine learning, are undoubtedly valuable in various medical applications, as demonstrated in this study. However, these methods rely on the availability of computers and stable internet, which are often lacking in remote areas. In addition to outlining the benefits of this method, the authors should also address its limitations, particularly in areas with limited resources, such as developing countries. This is particularly important considering the author's statement that most deaths due to ARBs occur in developing countries (Line 60-61). 3. Line 724: Is "metho" referring to a method? Reviewer #3: The manuscript is well-structured and tackles an important topic. However, It could be improved further by tackling the following: 1. Adding more actionable insights or suggestions for future research would further strengthen its contribution to the field 2. You acknowledge the high heterogeneity across the included studies, which is appropriate. However, I recommend elaborating on how this heterogeneity might affect the generalizability of the results. Discussing possible strategies for reducing the impact of heterogeneity (e.g., meta-regression or subgroup analysis) would be useful, even if they weren’t applied in the current review. 3. Consider grouping the results by key ML models or outcomes (e.g., by pathogens or prediction performance). This will help readers draw clearer connections between different studies ****** 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: Yes: Retno Wahyuningsih Reviewer #3: 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. Attachments Attachment Submitted filename: PONE D 24 19315 Reviewer comments.docx https://doi.org/10.1371/journal.pone.0319460.r002
Revision 1
18 Dec 2024 Author Response Dear Reviewers, We are grateful for the constructive comments you provided, which helped us to improve the manuscript significantly. Our responses to your comments are outlined below and highlighted in green in the new version. Reviewer #1: This is a well written report which provides a valuable contribution to avenues relevant to the exploration of AMR and AMS. Some sections require further clarification to provide context to the approach undertaken. 1. Line 56. Consider referencing the primary source rather than a secondary source. Response: The primary resource was referenced following the recommendations. 2. Line 210. Aspects of data extraction is presented, however ‘personalised data extraction methods’ needs to be better explained. Response: We thank the reviewer for this valuable comment. To address this, we have clarified the nature of the personalized data extraction methods used in our study. Specifically, we have detailed the development and use of customized and study-specific data extraction templates tailored to our research objectives. These templates facilitated the systematic collection of essential variables and publication-specific details, ensuring consistency and reliability across independent data collection efforts. The revised text can be found in Section 2.6 (Data Collection). 3. Line 228 to 231. A comprehensive narrative of the data analysis and reporting approach is needed. Response: We appreciate the reviewer’s insightful suggestion. In response, we have expanded the description of our data analysis and reporting approach in Section 2.8 (Summary Measurements). Specifically, we have detailed the use of descriptive statistics for summarizing continuous outcomes and provided additional information on how we assessed heterogeneity to determine the feasibility of meta-analysis. Furthermore, we have clarified that in the absence of sufficient homogeneity, a qualitative synthesis was conducted to provide a narrative summary of the findings. This revision offers a more comprehensive explanation of our approach and addresses the reviewer's concern. 4. Line 244. Figure 1: A more detailed PRISMA flow chart is needed. Response: We thank the reviewer for highlighting this point. In response, we have revised Figure 1 to include additional details for each stage of the study selection process. Considering that the study was submitted several months ago, we updated our search. We have detailed the records obtained in each database and provided clear descriptions of the reasons for exclusions during screening and full-text eligibility assessments, as well as the criteria used at each step. This enhanced figure offers a more transparent and detailed representation of the study selection methodology. Reviewer #2: 1. This manuscript heavily discuses on AUROC. It would be helpful if the authors provided a figure that summarizes AUROC, and their conclusions based on the various articles they studied. Response: A figure was provided following the recommendations. Fig. 2. Heatmap of Area Under the Receiver Operating Characteristic Curve (AUROC) Values for Various Machine Learning Models Across Studies. This figure presents a heatmap summarizing the AUROC values reported in different studies for various machine learning models. Each cell represents the AUROC value reported in the corresponding study for the specific model, with darker colors indicating higher AUROC values (closer to 1.0), signifying better model performance. Blank cells indicate cases where no AUROC value was reported. Studies are arranged along the vertical axis, while machine learning models are organized along the horizontal axis. This heatmap provides a visual comparison of model performance across studies and highlights models that consistently achieve high AUROC scores. 2. Digital methods, such as machine learning, are undoubtedly valuable in various medical applications, as demonstrated in this study. However, these methods rely on the availability of computers and stable internet, which are often lacking in remote areas. In addition to outlining the benefits of this method, the authors should also address its limitations, particularly in areas with limited resources, such as developing countries. This is particularly important considering the author's statement that most deaths due to ARBs occur in developing countries (Line 60-61). Response: The suggestion was recognized and the following text was added to the study limitations: Additionally, while digital methods such as machine learning have shown great promise in various medical applications, they rely heavily on access to computational resources and stable internet connectivity. This limitation is particularly significant in resource-limited settings, such as developing countries, where the burden of antimicrobial resistance and associated deaths is highest [3,4]. Future research should explore ways to adapt ML tools for offline use, develop lightweight algorithms that can operate on low-resource devices, or integrate these tools with existing healthcare systems in such regions. 3. Line 724: Is "metho" referring to a method? Response: The typo was resolved. Reviewer #3: The manuscript is well-structured and tackles an important topic. However, it could be improved further by tackling the following: 1. Adding more actionable insights or suggestions for future research would further strengthen its contribution to the field. Response: The suggestion was recognized and the following text was added to the study limitations: Future studies should aim to mitigate the impact of heterogeneity by employing strategies such as meta-regression or subgroup analyses to identify and address sources of variability. These methods, although not applied in the current review, could provide deeper insights into the influence of specific factors, such as study design, population characteristics, and ML algorithm choice, on outcomes. Additionally, while digital methods such as machine learning have shown great promise in various medical applications, they rely heavily on access to computational resources and stable internet connectivity. This limitation is particularly significant in resource-limited settings, such as developing countries, where the burden of antimicrobial resistance and associated deaths is highest [3,4]. Future research should explore ways to adapt ML tools for offline use, develop lightweight algorithms that can operate on low-resource devices, or integrate these tools with existing healthcare systems in such regions. Lastly, the study highlights the need for more actionable insights in future research. Specifically, efforts should focus on developing standardized reporting guidelines for ML studies to reduce variability, improving data quality and accessibility, and testing the scalability of ML models in real-world healthcare settings. Addressing these areas will enhance the practical applicability of ML in tackling AMR and other global health challenges. 2. You acknowledge the high heterogeneity across the included studies, which is appropriate. However, I recommend elaborating on how this heterogeneity might affect the generalizability of the results. Discussing possible strategies for reducing the impact of heterogeneity (e.g., meta-regression or subgroup analysis) would be useful, even if they weren’t applied in the current review. Response: The suggestion was recognized and the following text was added to the study limitations: The high heterogeneity observed in this study raises concerns about the generalizability of the results. Future studies should aim to mitigate the impact of heterogeneity by employing strategies such as meta-regression or subgroup analyses to identify and address sources of variability. These methods, although not applied in the current review, could provide deeper insights into the influence of specific factors, such as study design, population characteristics, and ML algorithm choice, on outcomes. Additionally, while digital methods such as machine learning have shown great promise in various medical applications, they rely heavily on access to computational resources and stable internet connectivity. This limitation is particularly significant in resource-limited settings, such as developing countries, where the burden of antimicrobial resistance and associated deaths is highest [3,4]. Future research should explore ways to adapt ML tools for offline use, develop lightweight algorithms that can operate on low-resource devices, or integrate these tools with existing healthcare systems in such regions. Lastly, the study highlights the need for more actionable insights in future research. Specifically, efforts should focus on developing standardized reporting guidelines for ML studies to reduce variability, improving data quality and accessibility, and testing the scalability of ML models in real-world healthcare settings. Addressing these areas will enhance the practical applicability of ML in tackling AMR and other global health challenges. 3. Consider grouping the results by key ML models or outcomes (e.g., by pathogens or prediction performance). This will help readers draw clearer connections between different studies. Response: A figure was provided following the recommendations. Fig. 2. Heatmap of Area Under the Receiver Operating Characteristic Curve (AUROC) Values for Various Machine Learning Models Across Studies. This figure presents a heatmap summarizing the AUROC values reported in different studies for various machine learning models. Each cell represents the AUROC value reported in the corresponding study for the specific model, with darker colors indicating higher AUROC values (closer to 1.0), signifying better model performance. Blank cells indicate cases where no AUROC value was reported. Studies are arranged along the vertical axis, while machine learning models are organized along the horizontal axis. This heatmap provides a visual comparison of model performance across studies and highlights models that consistently achieve high AUROC scores. Attachments Attachment Submitted filename: Responses.docx https://doi.org/10.1371/journal.pone.0319460.r003
7 Jan 2025 Decision Letter - Mohamed O Ahmed, Editor PONE-D-24-19315R1Machine learning for predicting antimicrobial resistance in critical and high-priority pathogens: A systematic review considering antimicrobial susceptibility tests in real-world healthcare settings.PLOS ONE Dear Dr. Ardila, 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: Comments from PLOS Editorial Office: We note that one or more reviewers has recommended that you cite specific previously published works. As always, we recommend that you please review and evaluate the requested works to determine whether they are relevant and should be cited. It is not a requirement to cite these works. We appreciate your attention to this request. ============================== Please submit your revised manuscript by Feb 21 2025 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, Mohamed O Ahmed, Ph.D 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. [Note: HTML markup is below. Please do not edit.] 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 Reviewer #3: 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 #2: Yes Reviewer #3: Partly ****** 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #2: Yes Reviewer #3: N/A ****** 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: (No Response) Reviewer #3: 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 Reviewer #3: No ****** 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: (No Response) Reviewer #3: 1. The introduction presents interesting statistics on the estimated number of infections and deaths, amongst others. It would be helpful if the authors stated clearly in each instance of the statistics whether it is global or related to developing countries. 2. Authors should reference and discuss previous systematic literature reviews in the domain and make a case for the relevance of the current review. Below are some examples of related reviews in the domain: a) Lv, G., & Wang, Y. (2024). Machine learning-based antibiotic resistance prediction models: An updated systematic review and meta-analysis. Technology and Health Care, (Preprint), 1-18. b) Sakagianni, A., Koufopoulou, C., Feretzakis, G., Kalles, D., Verykios, V. S., & Myrianthefs, P. (2023). Using machine learning to predict antimicrobial resistance―a literature review. Antibiotics, 12(3), 452. c) E. Elyan, A. Hussain, A. Sheikh, A. A. Elmanama, P. Vuttipittayamongkol and K. Hijazi, "Antimicrobial Resistance and Machine Learning: Challenges and Opportunities," in IEEE Access, vol. 10, pp. 31561-31577, 2022, doi: 10.1109/ACCESS.2022.3160213. 3. The author identifies data imbalance as one of the issues with existing data in the domain. Authors can, therefore, consider referencing the following article: Brown, S. A., Weyori, B. A., Adekoya, A. F., Kudjo, P. K., & Mensah, S. (2022). Predicting Blocking Bugs with Machine Learning Techniques: A Systematic Review. International Journal of Advanced Computer Science and Applications, 13(6). 4. Authors should suggest standardizing ML study reporting in AMR research, given the gaps identified in this work. 5. Authors should proofread their work thoroughly to correct grammatical errors. ****** 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: Yes: Retno Wahyuningsih Reviewer #3: Yes: Selasie Aformaley Brown,Ph.D ******** [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. https://doi.org/10.1371/journal.pone.0319460.r004
Revision 2
10 Jan 2025 Author Response Dear Reviewer, Our responses to your comments are outlined below and highlighted in green in the new version. Reviewer #3: 1. The introduction presents interesting statistics on the estimated number of infections and deaths, amongst others. It would be helpful if the authors stated clearly in each instance of the statistics whether it is global or related to developing countries. Response: We have carefully reviewed the introduction and incorporated the suggested clarifications. In the revised version of the manuscript, we have explicitly stated that the statistics cited are based on global data. For example: The projected number of deaths from bacterial infections by 2050 is described as occurring on a global scale. The data from 2019 on deaths linked to antibiotic-resistant bacteria (ARBs) is specified as worldwide. The statistics on methicillin-resistant S. aureus (MRSA) and other multidrug-resistant pathogens are clarified as global or occurring across the globe. 2. Authors should reference and discuss previous systematic literature reviews in the domain and make a case for the relevance of the current review. Below are some examples of related reviews in the domain: a) Lv, G., & Wang, Y. (2024). Machine learning-based antibiotic resistance prediction models: An updated systematic review and meta-analysis. Technology and Health Care, (Preprint), 1-18. b) Sakagianni, A., Koufopoulou, C., Feretzakis, G., Kalles, D., Verykios, V. S., & Myrianthefs, P. (2023). Using machine learning to predict antimicrobial resistance―a literature review. Antibiotics, 12(3), 452. c) E. Elyan, A. Hussain, A. Sheikh, A. A. Elmanama, P. Vuttipittayamongkol and K. Hijazi, "Antimicrobial Resistance and Machine Learning: Challenges and Opportunities," in IEEE Access, vol. 10, pp. 31561-31577, 2022, doi: 10.1109/ACCESS.2022.3160213. Response: Thank you for your thoughtful suggestion to include additional systematic literature reviews in our manuscript to further support the relevance of our current review. We appreciate the references provided and acknowledge their contributions to the field. However, we would like to respectfully clarify that our systematic review already incorporates and discusses several relevant and high-impact systematic reviews and meta-analyses addressing antimicrobial resistance (AMR) and machine learning-based prediction models. These include: - O’Neill J (2016). Tackling Drug-Resistant Infections Globally. - Antimicrobial Resistance Collaborators. Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis. Lancet. 2022;399(10325):629-655. doi: 10.1016/S0140-6736(21)02724-0 - Tang et al. (2022) – A systematic review and meta-analysis on machine learning in predicting antimicrobial resistance [39]. - Pormohammad et al. (2019) – A systematic review and meta-analysis on antibiotic resistance in E. coli strains isolated from multiple sources [6]. - Christodoulou et al. (2019) – A systematic review evaluating machine learning versus logistic regression for clinical prediction models [43]. - Beunza et al. (2019) – A comparison of machine learning algorithms for predicting clinical events, [44]. - Sufriyana et al. (2020) – A systematic review and meta-analysis comparing multivariable logistic regression with machine learning algorithms [45]. - Delpino et al. (2022) – A systematic review on machine learning applications for predicting chronic diseases [52]. Additionally, we have included foundational reviews such as O’Neill (2016) [2] and the Antimicrobial Resistance Collaborators (2022) [3], which provide a broader context of AMR's global impact and underscore the urgency of predictive approaches. Given the comprehensive nature of these cited works, we believe our manuscript already provides a well-rounded background without overwhelming the reader with excessive references. 3. The author identifies data imbalance as one of the issues with existing data in the domain. Authors can, therefore, consider referencing the following article: Brown, S. A., Weyori, B. A., Adekoya, A. F., Kudjo, P. K., & Mensah, S. (2022). Predicting Blocking Bugs with Machine Learning Techniques: A Systematic Review. International Journal of Advanced Computer Science and Applications, 13(6). Response: We appreciate your insightful suggestion to include the recommended reference by Brown et al. (2022) to strengthen our discussion on data imbalance challenges in machine learning applications. In response, we have incorporated the reference into the discussion section of our revised manuscript. Specifically, we highlight how the study by Brown et al. underscores the impact of imbalanced datasets on evaluation metrics and emphasizes the need for more robust validation approaches. This addition provides a broader perspective on addressing data imbalance issues across domains involving machine learning models. 4. Authors should suggest standardizing ML study reporting in AMR research, given the gaps identified in this work. Response: We have revised the discussion section to explicitly emphasize the importance of developing and adopting standardized reporting guidelines for ML studies. This addition highlights the necessity of reducing variability, ensuring methodological transparency, and facilitating reproducibility in future AMR research. 5. Authors should proofread their work thoroughly to correct grammatical errors. Response: A thorough review of the manuscript was conducted, and grammatical errors were corrected. Attachments Attachment Submitted filename: Responses Reviewer 3.docx https://doi.org/10.1371/journal.pone.0319460.r005
4 Feb 2025 Decision Letter - Mohamed O Ahmed, Editor Machine learning for predicting antimicrobial resistance in critical and high-priority pathogens: A systematic review considering antimicrobial susceptibility tests in real-world healthcare settings. PONE-D-24-19315R2 Dear Dr. Ardila, 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 will be generated when your article is formally accepted. Please note, if your institution has a publishing partnership with PLOS and your article meets the relevant criteria, all or part of your publication costs will be covered. Please make sure your user information is up-to-date by logging into Editorial Manager at Editorial Manager® and clicking the ‘Update My Information' link at the top of the page. If you have any questions relating to publication charges, 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, Mohamed O Ahmed, Ph.D Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0319460.r006
Formally Accepted
Acceptance Letter - Mohamed Ahmed, Editor PONE-D-24-19315R2 PLOS ONE Dear Dr. Ardila, I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now being handed over to our production team. At this stage, our production department will prepare your paper for publication. This includes ensuring the following: * All references, tables, and figures are properly cited * All relevant supporting information is included in the manuscript submission, * There are no issues that prevent the paper from being properly typeset If revisions are needed, the production department will contact you directly to resolve them. If no revisions are needed, you will receive an email when the publication date has been set. At this time, we do not offer pre-publication proofs to authors during production of the accepted work. Please keep in mind that we are working through a large volume of accepted articles, so please give us a few weeks to review your paper and let you know the next and final steps. Lastly, if your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. 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. If we can help with anything else, please email us at customercare@plos.org. Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staff on behalf of Dr. Mohamed O Ahmed Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0319460.r007

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