Increasing transparency in machine learning through bootstrap simulation and shapely additive explanations

Alexander A. Huang; Samuel Y. Huang

doi:10.1371/journal.pone.0281922

Peer Review History

Original SubmissionNovember 23, 2022
29 Dec 2022 Decision Letter - Loredana Bellantuono, Editor PONE-D-22-32361Increasing Transparency in Machine Learning through Bootstrap Simulation and Shapely Additive ExplanationsPLOS ONE Dear Dr. Huang, 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. The reviewers' comments are appended below. Please submit your revised manuscript by Feb 12 2023 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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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: Yes 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: This paper addresses the problem of machine learning explainability to medicine. Through bootstrap simulation and Shapley Additive exPlanations (SHAP), the study aims to increase model transparency and reliability and improve model selection. Strengths: 1) The paper presented an interesting approach and evaluated the chosen dataset well. 2) The system presented has potential for wide-scale deployment, tested against many strong baselines. Weaknesses: Although the paper presented interesting contributions towards the problem of machine learning explainability in health, a few areas still need some improvement. • Abstract: In the abstract, only the XGBoost was mentioned as the model used, but other models, such as Random Forest, Artificial Neural Network etc was used in the study. • Compare results with findings in literature: It might be worth comparing the findings of this study with that in the literature. It is essential to know what was learnt and the methodology used. • SHAP over other tools: Although ML explainability is relatively new, several tools have been explored in the literature. It might be worth asking why the choice of SHAP over others, such as ELI5, LIME and Yellowbrick, etc. • Sketchy literature: For an emerging study, the literature appears insufficient missing prior work in this area. How is this study different from the below (cite if relevant): o Dave, D., Naik, H., Singhal, S. and Patel, P., 2020. Explainable ai meets healthcare: A study on heart disease dataset. arXiv preprint arXiv:2011.03195. o Shi, H., Yang, D., Tang, K., Hu, C., Li, L., Zhang, L., Gong, T. and Cui, Y., 2022. Explainable machine learning model for predicting the occurrence of postoperative malnutrition in children with congenital heart disease. Clinical Nutrition, 41(1), pp.202-210. o Lu, S., Chen, R., Wei, W., Belovsky, M. and Lu, X., 2021. Understanding Heart Failure Patients EHR Clinical Features via SHAP Interpretation of Tree-Based Machine Learning Model Predictions. In AMIA Annual Symposium Proceedings (Vol. 2021, p. 813). American Medical Informatics Association. o Zhou, Y., Chen, S., Rao, Z., Yang, D., Liu, X., Dong, N. and Li, F., 2021. Prediction of 1-year mortality after heart transplantation using machine learning approaches: A single-center study from China. International Journal of Cardiology, 339, pp.21-27. o Chaves, J.M.Z., Chaudhari, A.S., Wentland, A.L., Desai, A.D., Banerjee, I., Boutin, R.D., Maron, D.J., Rodriguez, F., Sandhu, A.T., Jeffrey, R.B. and Rubin, D., 2021. Opportunistic assessment of ischemic heart disease risk using abdominopelvic computed tomography and medical record data: a multimodal explainable artificial intelligence approach. medRxiv. o Obaido, G., Ogbuokiri, B., Swart, T.G., Ayawei, N., Kasongo, S.M., Aruleba, K., Mienye, I.D., Aruleba, I., Chukwu, W., Osaye, F. and Egbelowo, O.F., 2022. An Interpretable Machine Learning Approach for Hepatitis B Diagnosis. Applied Sciences, 12(21), p.11127. Reviewer #2: The paper is clearly written, reproducible and technically fine, in terms of statistical analyses performed. My concern, beyond the limitations that the authors have addressed is to discuss the approach with more complex designs (data cohorts, variables) that the one in use. The design is the following: Independent Variables: Demographic covariates (age and sex). Clinical covariates (Resting blood pressure, fasting blood sugar, cholesterol, resting electrocardiogram (ECG), presence of Angina, and maximum heart rate). Dependent variable: heart disease, as diagnosed by a clinician. Especially from EHR, one expects to find a mix of information sources with data of different nature, and this aspect of multimodality complicates model selection and may require different strategies to measure performance and explanation. This might calls for more heterogeneous data to be analyzed with methods to be compared, which is in part one of the limitations that the authors have reported. The authors should discuss more in depth this part. ****** 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. 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Revision 1
30 Jan 2023 Author Response 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 Thank you reviewers for listing the documents leading to the correction of the style requirements. 2. Thank you for including your ethics statement: "N/A". (1) For studies reporting research involving human participants, PLOS ONE requires authors to confirm that this specific study was reviewed and approved by an institutional review board (ethics committee) before the study began. Please provide the specific name of the ethics committee/IRB that approved your study, or explain why you did not seek approval in this case. 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If you are reporting a retrospective study of medical records or archived samples, please ensure that you have discussed whether all data were fully anonymized before you accessed them and/or whether the IRB or ethics committee waived the requirement for informed consent. If patients provided informed written consent to have data from their medical records used in research, please include this information Once you have amended this/these statement(s) in the Methods section of the manuscript, please add the same text to the “Ethics Statement” field of the submission form (via “Edit Submission”). For additional information about PLOS ONE ethical requirements for human subjects research, please refer to http://journals.plos.org/plosone/s/submission-guidelines#loc-human-subjects-research. Thank you for advising on PLOS ONE requirements for ethical statements. We have updated these statements in the Methods and “ethics statement” field as well as the cover letter. 3. 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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. We have added new references to the list as advised by reviewers. [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: Yes 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: This paper addresses the problem of machine learning explainability to medicine. Through bootstrap simulation and Shapley Additive exPlanations (SHAP), the study aims to increase model transparency and reliability and improve model selection. Strengths: 1) The paper presented an interesting approach and evaluated the chosen dataset well. 2) The system presented has potential for wide-scale deployment, tested against many strong baselines. Weaknesses: Although the paper presented interesting contributions towards the problem of machine learning explainability in health, a few areas still need some improvement. • Abstract: In the abstract, only the XGBoost was mentioned as the model used, but other models, such as Random Forest, Artificial Neural Network etc was used in the study. Thank you for bringing this to our attention we have made the change. • Compare results with findings in literature: It might be worth comparing the findings of this study with that in the literature. It is essential to know what was learnt and the methodology used. Thank you for bringing this to our attention, we have made the change. • SHAP over other tools: Although ML explainability is relatively new, several tools have been explored in the literature. It might be worth asking why the choice of SHAP over others, such as ELI5, LIME and Yellowbrick, etc. We did a literature search and found SHAP to be most prevalent over others. Additionally, SHAP has packages within R and python that are compatible with machine learning methods used in this study. Further research is needed to compare the efficacy of models such as SHAP with ELI5, LIME, and Yellowbrick, etc. Goal of paper is to generate a workflow for machine learning, taking into account bootstrapping to generate the distribution for model accuracy statistics, evaluate the feature importances of all our covariates, and highlight the utility of model explanation, only SHAP was used in the study, but we agree with the suggestions above and will do it as a line of inquiry in our next. • Sketchy literature: For an emerging study, the literature appears insufficient missing prior work in this area. How is this study different from the below (cite if relevant): o Dave, D., Naik, H., Singhal, S. and Patel, P., 2020. Explainable ai meets healthcare: A study on heart disease dataset. arXiv preprint arXiv:2011.03195. o Shi, H., Yang, D., Tang, K., Hu, C., Li, L., Zhang, L., Gong, T. and Cui, Y., 2022. Explainable machine learning model for predicting the occurrence of postoperative malnutrition in children with congenital heart disease. Clinical Nutrition, 41(1), pp.202-210. o Lu, S., Chen, R., Wei, W., Belovsky, M. and Lu, X., 2021. Understanding Heart Failure Patients EHR Clinical Features via SHAP Interpretation of Tree-Based Machine Learning Model Predictions. In AMIA Annual Symposium Proceedings (Vol. 2021, p. 813). American Medical Informatics Association. o Zhou, Y., Chen, S., Rao, Z., Yang, D., Liu, X., Dong, N. and Li, F., 2021. Prediction of 1-year mortality after heart transplantation using machine learning approaches: A single-center study from China. International Journal of Cardiology, 339, pp.21-27. o Chaves, J.M.Z., Chaudhari, A.S., Wentland, A.L., Desai, A.D., Banerjee, I., Boutin, R.D., Maron, D.J., Rodriguez, F., Sandhu, A.T., Jeffrey, R.B. and Rubin, D., 2021. Opportunistic assessment of ischemic heart disease risk using abdominopelvic computed tomography and medical record data: a multimodal explainable artificial intelligence approach. medRxiv. o Obaido, G., Ogbuokiri, B., Swart, T.G., Ayawei, N., Kasongo, S.M., Aruleba, K., Mienye, I.D., Aruleba, I., Chukwu, W., Osaye, F. and Egbelowo, O.F., 2022. An Interpretable Machine Learning Approach for Hepatitis B Diagnosis. Applied Sciences, 12(21), p.11127. Thank you for suggesting these papers. They have been helpful in revising the introduction and discussion to add greater depth. All of these papers are references either in the introduction or the discussion. What our study brings to the literature is a comprehensive framework for machine learning for medical applications. They consist of an initial machine learning selection methodology that utilizes bootstrap simulation to compute confidence intervals of numerous model accuracy statistics, which is not readily done by current studies. Furthermore, this methodology incorporates multiple feature importance statistics for feature selection. Lastly, the clinically relevant features within the model can be visualized accurately using SHAP. This methodology will streamline the reporting of machine learning by first highlighting the variability of machine learning accuracy statistics even when utilizing the same dataset, using feature importance statistics to understand how the model values each feature and finally utilizing SHAP visualization to understand how the model is generating predictions from each covariate. Reviewer #2: The paper is clearly written, reproducible and technically fine, in terms of statistical analyses performed. My concern, beyond the limitations that the authors have addressed is to discuss the approach with more complex designs (data cohorts, variables) that the one in use. The design is the following: Independent Variables: Demographic covariates (age and sex). Clinical covariates (Resting blood pressure, fasting blood sugar, cholesterol, resting electrocardiogram (ECG), presence of Angina, and maximum heart rate). Dependent variable: heart disease, as diagnosed by a clinician. Especially from EHR, one expects to find a mix of information sources with data of different nature, and this aspect of multimodality complicates model selection and may require different strategies to measure performance and explanation. This might calls for more heterogeneous data to be analyzed with methods to be compared, which is in part one of the limitations that the authors have reported. The authors should discuss more in depth this part. The authors would like to thank the reviewer for highlighting such an important further analysis. The authors have addressed this in the limitations and discussed it more in depth. Attachments Attachment Submitted filename: SHAPLY Revisions.docx https://doi.org/10.1371/journal.pone.0281922.r002
5 Feb 2023 Decision Letter - Loredana Bellantuono, Editor Increasing Transparency in Machine Learning through Bootstrap Simulation and Shapely Additive Explanations PONE-D-22-32361R1 Dear Dr. Huang, 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, Loredana Bellantuono, Ph.D. Academic Editor PLOS ONE Additional Editor Comments (optional): Reviewers' comments: https://doi.org/10.1371/journal.pone.0281922.r003
Formally Accepted
10 Feb 2023 Acceptance Letter - Loredana Bellantuono, Editor PONE-D-22-32361R1 Increasing Transparency in Machine Learning through Bootstrap Simulation and Shapely Additive Explanations Dear Dr. Huang: I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. 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 plosone@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. Loredana Bellantuono Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0281922.r004

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