Leveraging nonlinear relationships and interactions to improve 30-day pneumonia readmission machine learning models

Eric M. Mortensen; Nkiruka Atuegwu; Shane J. Sacco

doi:10.1371/journal.pone.0349804

Peer Review History

Original SubmissionJanuary 22, 2026
17 Mar 2026 Decision Letter - Oliver Schildgen, Editor -->PONE-D-26-02589-->-->Leveraging Nonlinear Relationships and Interactions to Improve 30-Day Pneumonia Readmission Machine Learning Models-->-->PLOS One Dear Dr. Mortensen, 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 revise according to the reviewer's comments. Please submit your revised manuscript by May 01 2026 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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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 manuscript addresses an important and persistent challenge in health services research: the prediction of 30-day readmissions following pneumonia hospitalization. The authors present a rigorously conducted comparison of traditional logistic regression with more advanced machine learning approaches (XGBoost and deep neural networks) using rich longitudinal EMR data that includes continuous clinical variables. The work is methodologically sound, clearly written, and transparent in reporting negative findings—an important contribution given publication bias toward performance gains. While the primary hypothesis that advanced ML models would outperform logistic regression was not supported, the manuscript offers meaningful insights into why performance plateaus occur and identifies novel predictors (notably BUN and substance abuse). The study is valuable, but several conceptual, methodological, and interpretive issues should be addressed to strengthen its impact and clarity. A. Major Comments 1. Conceptual Framing: “Nonlinearity” vs. “Predictive Ceiling” Strength: The rationale for exploring nonlinear relationships using ML models is well articulated and grounded in the literature. The post hoc spline analysis of BUN is a strong and thoughtful addition. Concern: The manuscript continues to frame the study primarily as an evaluation of whether nonlinear modeling improves prediction, even though the results clearly suggest a predictive ceiling driven by limitations of EMR data rather than model choice. Recommendation: Reframe the manuscript more explicitly around the concept of data-limited prediction, rather than model-limited prediction. This would better align the Introduction, Results, and Discussion. Consider stating earlier (in the Introduction or Methods) that an alternative goal is to characterize what ML models learn differently, even when performance is similar. 2. Feature Pre-Selection and Its Impact on Machine Learning Models Strength: The marginal screening procedure is clearly described and appropriately adjusted for multiple testing. Concern: The use of univariate screening (p<0.10 with BH correction) prior to ML modeling may constrain the ability of ML models to discover interactions and nonlinear effects, undermining the core premise of the study. This is particularly relevant for DNNs and XGBoost, which are designed to handle high-dimensional feature spaces and correlated predictors. Recommendation: Explicitly acknowledge this as a limitation in the Discussion. Clarify why marginal screening was preferred over embedded feature selection methods (e.g., L1 regularization, tree-based importance). Consider adding a sensitivity analysis (if feasible) comparing models with and without pre-screening, or at least discuss how this choice may have attenuated ML advantages. 3. Interpretation of SHAP Values for Binary Predictors Strength: The use of SHAP values enhances model interpretability and is a major strength of the paper. Concern: The approach of calculating SHAP values only among patients with endorsed binary predictors (to avoid washout) is unconventional and may inflate perceived importance for rare variables (e.g., drug abuse at 1.1%). Recommendation: Provide a stronger methodological justification for this approach. Discuss how this decision affects comparability across predictors and models. Consider reporting prevalence-adjusted SHAP summaries or including a sensitivity comparison using standard SHAP aggregation. 4. Readmission Outcome Definition and Noise Strength: The authors appropriately align the outcome definition with CMS quality measures. Concern: All-cause readmission is acknowledged as noisy, but the implications are understated. Given that pneumonia-related factors account for a minority of readmissions, the models may be penalized for predicting events driven by post-discharge social or behavioral factors absent from the EMR. Recommendation: Expand the Discussion on outcome heterogeneity and label noise. Explicitly consider whether pneumonia-specific readmissions (even as a secondary analysis) might yield different insights. Frame the modest AUROC/AUPRC as partly a consequence of outcome misalignment with available predictors. 5. Clinical Translation and Actionability Strength: The manuscript thoughtfully discusses implementation challenges and resource allocation. Concern: Despite modest PPV, the manuscript stops short of proposing a clear operational use case for the models (e.g., triage vs. screening vs. layered intervention). Recommendation: Strengthen the Discussion by explicitly stating: What clinical decision this model should support. Whether it is best suited for rule-out, risk stratification, or resource prioritization. How clinicians should interpret a “high-risk” label given the PPV of ~0.16. B. Minor Comments 1. Abstract Consider clarifying that performance was similar rather than simply “did not improve,” to avoid implying methodological failure. The phrase “novel predictors” may overstate novelty; consider “previously underemphasized predictors.” 2. Methods Clarify whether continuous variables were normalized or transformed prior to DNN training. Provide additional detail on DNN architecture (layers, activation functions) in the main text or supplement. 3. Results Table 2 would benefit from explicitly stating that confidence intervals overlap substantially across models. Consider adding calibration metrics (e.g., calibration slope or Brier score), as these are clinically relevant even when AUROC is modest. 4. Figures Figure 2 is strong conceptually; adding a brief clinical annotation (e.g., normal BUN range) directly on the plot would enhance interpretability. Ensure consistent terminology: “SHaply” → “SHapley” in Figure 1 caption. 5. Language and Style The manuscript is well written; minor tightening is possible in the Discussion to reduce repetition around EMR limitations. Consider reducing redundancy between Discussion paragraphs on social determinants of health. Reviewer #2: It is a excellent study in the field of pneumonia. It will helps the physician treating pneumonia and can help them identifying patients of pneumonia which are at mores risk for readmission. This knowledge can help them reducing risk for readmission and help in keeping patients out of hospital. ****** -->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.] To ensure your figures meet our technical requirements, please review our figure guidelines: https://journals.plos.org/plosone/s/figures You may also use PLOS’s free figure tool, NAAS, to help you prepare publication quality figures: https://journals.plos.org/plosone/s/figures#loc-tools-for-figure-preparation. NAAS will assess whether your figures meet our technical requirements by comparing each figure against our figure specifications. https://doi.org/10.1371/journal.pone.0349804.r001
Revision 1
22 Apr 2026 Author Response Response to Reviewers Reviewer 1 A. Major Comments 1. Conceptual Framing: “Nonlinearity” vs. “Predictive Ceiling.” Strength: The rationale for exploring nonlinear relationships using ML models is well articulated and grounded in the literature. The post hoc spline analysis of BUN is a strong and thoughtful addition. Concern: The manuscript continues to frame the study primarily as an evaluation of whether nonlinear modeling improves prediction, even though the results clearly suggest a predictive ceiling driven by limitations of EMR data rather than model choice. Recommendation: Reframe the manuscript more explicitly around the concept of data-limited prediction, rather than model-limited prediction. This would better align the Introduction, Results, and Discussion. Consider stating earlier (in the Introduction or Methods) that an alternative goal is to characterize what ML models learn differently, even when performance is similar. Author response (AR): Thank you for your suggestion. We agree that highlighting data limitations in the introduction would create a more consistent narrative. However, we still want to keep our main hypotheses regarding model limitations. Therefore, we have added a discussion of data limitations in our introduction on lines 84-87. “One reason for this similarity may be that these models excluded continuous variables, which could have benefited from nonlinear or interaction effects (e.g., vital signs or laboratory findings). Given that the data available in medical records (required for real-time prediction in healthcare settings) is generally limited, discovering these additional relationships could be even more important for model development.” 2. Feature Pre-Selection and Its Impact on Machine Learning Models Strength: The marginal screening procedure is clearly described and appropriately adjusted for multiple testing. Concern: The use of univariate screening (p<0.10 with BH correction) prior to ML modeling may constrain the ability of ML models to discover interactions and nonlinear effects, undermining the core premise of the study. This is particularly relevant for DNNs and XGBoost, which are designed to handle high-dimensional feature spaces and correlated predictors. Recommendation: Explicitly acknowledge this as a limitation in the Discussion. Clarify why marginal screening was preferred over embedded feature selection methods (e.g., L1 regularization, tree-based importance). Consider adding a sensitivity analysis (if feasible) comparing models with and without pre-screening, or at least discuss how this choice may have attenuated ML advantages. AR: We acknowledge this point. We considered the trade-off between screening (to reduce overfitting to the training data) and using the full dataset (to enable machine learning models to detect interactions). In response, we added a post-hoc analysis that reruns our models without screening on lines 273-283. “To help improve the generalizability of our models, we carried out an initial marginal screening process, which involves a tradeoff because it may miss variables that influence importance only through interactions. Since XGBoost and DNN models can evaluate feature importance natively and still consider variables important only in interactions, we reran our primary analyses without the marginal screening process (Supplement 4). In this post hoc analysis, we observed that the performance of all models generally stayed the same, although their metrics were slightly worse. The logistic regression model was affected the most, with the average AUROC decreasing from 0.63 to 0.59, and the percentage of patients with readmissions in the bottom decile of risk scores increasing from 4% to 9%.” We also now discuss this limitation on lines 367-372. “Finally, we lightly screened our features to help trained models better generalize to the testing data. When removing this step in our post hoc analysis, it seemed particularly important for the logistic regression model, while the advanced ML models showed little change. We recognize that more sophisticated screening methods exist that could have affected our results, which remains a limitation of this work.” 3. Interpretation of SHAP Values for Binary Predictors Strength: The use of SHAP values enhances model interpretability and is a major strength of the paper. Concern: The approach of calculating SHAP values only among patients with endorsed binary predictors (to avoid washout) is unconventional and may inflate perceived importance for rare variables (e.g., drug abuse at 1.1%). Recommendation: Provide a stronger methodological justification for this approach. Discuss how this decision affects comparability across predictors and models. Consider reporting prevalence-adjusted SHAP summaries or including a sensitivity comparison using standard SHAP aggregation. AR: Thank you; we understand your concern. We provided current SHAP summaries to highlight the importance of less common yet critical diagnosis codes that are significant for clinical judgment. SHAPs in this context mainly reflect model coefficients from logistic regression (conditional importance). However, we recognize that global importance, which includes prevalence, would also be valuable. In response, we expand our explanation for conditional SHAPs on lines 173-177. “For binary predictors, we calculated SHAP values within patients who had that variable endorsed to highlight clinically important diagnoses. Most of these diagnoses occur only in a small subset of patients (e.g., <10%). Their significance would be diminished or “washed out” by low prevalence in overall SHAP value calculations due to zero contributions from most patients.” We also provide results summarizing global SHAP values on lines 227-234 and in Supplementary File 3. “Given that we calculated binary variable SHAP values only within patients who endorsed that variable, we then recalculated global SHAP values without this restriction (Supplement 3). As expected, almost all diagnoses dropped out of the list of most important predictors and were replaced by continuous variables that were present in all patients. This was an exception for employment status in the logistic regression model and for fluid/electrolyte disorders in the XGBoost model. The top predictors within the DNN model remained largely unchanged since they were already primarily continuous variables.” Lastly, we revised lines 296-299. “All of our models identified substance abuse as an important predictor of readmission among the few patients with this diagnosis code (1.1%); for the overall sample, vitals and laboratory findings were most important based on global SHAP values.” 4. Readmission Outcome Definition and Noise Strength: The authors appropriately align the outcome definition with CMS quality measures. Concern: All-cause readmission is acknowledged as noisy, but the implications are understated. Given that pneumonia-related factors account for a minority of readmissions, the models may be penalized for predicting events driven by post-discharge social or behavioral factors absent from the EMR. Recommendation: Expand the Discussion on outcome heterogeneity and label noise. Explicitly consider whether pneumonia-specific readmissions (even as a secondary analysis) might yield different insights. Frame the modest AUROC/AUPRC as partly a consequence of outcome misalignment with available predictors. AR: This is an excellent point. In response, we provide additional discussion on lines 357-364. “Third, our outcome measure—all-cause readmission—includes admissions unrelated to the index pneumonia hospitalization, which may introduce noise into our models. For example, some patients may be readmitted for unrelated injuries (e.g., falls, accidents) rather than for pneumonia sequelae. In such cases, admission characteristics reflecting pneumonia severity would not be informative. Consequently, the modest performance metrics (e.g., AUROC, AUPRC) may be partly attributable to a misalignment between admission characteristics and unrelated readmissions in some patients.” 5. Clinical Translation and Actionability Strength: The manuscript thoughtfully discusses implementation challenges and resource allocation. Concern: Despite modest PPV, the manuscript stops short of proposing a clear operational use case for the models (e.g., triage vs. screening vs. layered intervention). Recommendation: Strengthen the Discussion by explicitly stating: What clinical decision this model should support. Whether it is best suited for rule-out, risk stratification, or resource prioritization. How clinicians should interpret a “high-risk” label given the PPV of ~0.16. AR: We agree that this deserves some elaboration, although it is difficult to provide a definitive guideline when such models are not currently deployed (i.e., unknown resource allocation, workflow integration, etc). In response, we have expanded our discussion to include more details on lines 344-351. “Although we are hesitant to provide specific guidelines for implementation since these algorithms are not currently used by any hospitals and logistics such as resource allocation and workflow integration remain uncertain, these models could still be used for both risk stratification and resource allocation. For example, labeling nearly half of the sample as high risk (39-43% at the Youden Index) resulted in PPVs between 0.15 and 0.17. These patients could have brief discharge conversations. Labeling 10% of the sample resulted in PPVs between 0.19 and 0.20. These patients could also receive follow-up phone calls.” B. Minor Comments 1. Abstract Consider clarifying that performance was similar rather than simply “did not improve,” to avoid implying methodological failure. The phrase “novel predictors” may overstate novelty; consider “previously underemphasized predictors.” AR: Thanks, done. We also modified line 294, which mentioned novel. 2. Methods Clarify whether continuous variables were normalized or transformed prior to DNN training. Provide additional detail on DNN architecture (layers, activation functions) in the main text or supplement. AR: We have added that we normalized variables in Supplement 2. We already provide extensive details in Supplement 2 on the architecture, layer purposes, and activation functions. We would be happy to provide more details if you could specify them, please. 3. Results Table 2 would benefit from explicitly stating that confidence intervals overlap substantially across models. AR: This makes sense. We have added these details on lines 209-211. “Our more advanced ML models performed similarly, providing comparable average metrics and mostly overlapping confidence intervals across all models.” Consider adding calibration metrics (e.g., calibration slope or Brier score), as these are clinically relevant even when AUROC is modest. AR: Good point. In response, we have added this information to the Methods section (lines 165-167), Results section (211-216), and Table 2. “To further evaluate discrimination and calibration, we visually examined calibration plots and calculated Brier scores (0=perfect calibration and discrimination).” “When visually inspecting calibration plots along with Brier scores, logistic regression seemed to have the best calibration according to the Brier score, although all models performed relatively similarly. Notably, from calibration plots (data not shown), all models consistently predicted higher probabilities of readmission than actually observed across the risk spectrum.” 4. Figures Figure 2 is strong conceptually; adding a brief clinical annotation (e.g., normal BUN range) directly on the plot would enhance interpretability. AR: Good idea. Added. Ensure consistent terminology: “SHaply” → “SHapley” in Figure 1 caption. AR: Thank you for this suggestion. Fixed. 5. Language and Style The manuscript is well written; minor tightening is possible in the Discussion to reduce repetition around EMR limitations. Consider reducing redundancy between Discussion paragraphs on social determinants of health. AR: We agree. In response, we have significantly reduced this section on lines 319-325. We also removed the paragraph related to treatment planning for patients with drug abuse, as it only affected a few patients. “However, the consistent performance of our models and other published studies suggests that the modest predictive accuracy may be due to the inherent limitations of the data available in electronic medical records. EMRs lack key social determinants of health (e.g., housing stability, health literacy, lifestyle habits, environmental factors), which can affect readmission risk. Therefore, collecting some of this information during the intake process could improve the algorithms.” Reviewer #2: It is a excellent study in the field of pneumonia. It will helps the physician treating pneumonia and can help them identifying patients of pneumonia which are at mores risk for readmission. This knowledge can help them reducing risk for readmission and help in keeping patients out of hospital. Attachments Attachment Submitted filename: Response to Reviewers.docx https://doi.org/10.1371/journal.pone.0349804.r002
5 May 2026 Decision Letter - Oliver Schildgen, Editor Leveraging Nonlinear Relationships and Interactions to Improve 30-Day Pneumonia Readmission Machine Learning Models PONE-D-26-02589R1 Dear Dr. Mortensen, 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. For questions related to billing, please contact billing support. 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, Oliver Schildgen Academic Editor PLOS One Additional Editor Comments (optional): 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: (No Response) ****** -->3. Has the statistical analysis been performed appropriately and rigorously? --> Reviewer #1: (No Response) ****** -->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: (No Response) ****** -->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: (No Response) ****** -->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: (No Response) ****** -->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 ******** https://doi.org/10.1371/journal.pone.0349804.r003
Formally Accepted
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