Expected length of stay in residential aged care facilities in Australia: Assessing the impact of dementia using machine learning

Alexander Fracalossi; Jinhui Zhang; Evelyn Lee; Yanlin Shi

doi:10.1371/journal.pone.0321612

Peer Review History

Original SubmissionDecember 13, 2024
13 Dec 2024 Author Response https://doi.org/10.1371/journal.pone.0321612.r001
30 Dec 2024 Decision Letter - Eugene Demidenko, Editor PONE-D-24-53339Expected Length of Stay in Residential Aged Care Facilities in Australia: Assessing the Impact of Dementia using Machine Learning PLOS ONE Dear Dr. Lee, 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. Here are my comments and concerns. The paper discusses modeling of Long Of Stay (LOS) of elderly population in Australia. The rule of thumb is that Machine Learning (ML) is applicable after the authors demonstrate that simple and well-interpretable classic methods do not work. Although the authors report R-squared I have to remind that this metric increases with the number of parameters and model complexity in general. This means that R-squared is not a fair metric to compare linear regression and the Random Forest regression. Since the authors aim to predict LOS another metric, such as the Root Mean Square of LOS prediction via the leave-one-out technique should be employed. The difference between states is not addressed. Mixed model with random intercepts that represent the state-specific LOS is easy to apply, and I recommend employing this model. I wonder how the state-specific LOS can be accounted using the ML methodology. The nonlinearity of LOS versus Age is not addressed in regression analysis. I suggest showing the cross plot (Age,LOS) and displaying linear, quadratic, and a nonparametric regression (such as ‘loess’ in R) to understand how nonlinear the relationship is. This univariate analysis must be repeated for all other predictors. The authors must explain/interpret ‘Importance’ of the variables in Figs 4 and 5. Importance with respect to what? How to explain Importance to Medical Doctors? Why Importance scale is different in the figures? Data are not available: “No - some restrictions will apply.” It’s unclear why de-identified data cannot be accessed. I have to remind the authors that the data availability is a major condition for research transparency and results reproducibility. Please submit your revised manuscript by Feb 13 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'. 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Please include captions for your Supporting Information files at the end of your manuscript, and update any in-text citations to match accordingly. Please see our Supporting Information guidelines for more information: http://journals.plos.org/plosone/s/supporting-information. Additional Editor Comments: The paper discusses modeling of Long Of Stay (LOS) of elderly population in Australia. The rule of thumb is that Machine Learning (ML) is applicable after the authors demonstrate that simple and well-interpretable classic methods do not work. Although the authors report R-squared I have to remind that this metric increases with the number of parameters and model complexity in general. This means that R-squared is not a fair metric to compare linear regression and the Random Forest regression. Since the authors aim to predict LOS another metric, such as the Root Mean Square of LOS prediction via the leave-one-out technique should be employed. The difference between states is not addressed. Mixed model with random intercepts that represent the state-specific LOS is easy to apply, and I recommend employing this model. I wonder how the state-specific LOS can be accounted using the ML methodology. The nonlinearity of LOS versus Age is not addressed in regression analysis. I suggest showing the cross plot (Age,LOS) and displaying linear, quadratic, and a nonparametric regression (such as ‘loess’ in R) to understand how nonlinear the relationship is. This univariate analysis must be repeated for all other predictors. The authors must explain/interpret ‘Importance’ of the variables in Figs 4 and 5. Importance with respect to what? How to explain Importance to Medical Doctors? Why Importance scale is different in the figures? Data are not available: “No - some restrictions will apply.” It’s unclear why de-identified data cannot be accessed. I have to remind the authors that the data availability is a major condition for research transparency and results reproducibility. [Note: HTML markup is below. Please do not edit.] [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.0321612.r002
Revision 1
11 Jan 2025 Author Response The paper discusses modelling of Length of Stay (LOS) of elderly population in Australia. 1. The rule of thumb is that Machine Learning (ML) is applicable after the authors demonstrate that simple and well-interpretable classic methods do not work. Although the authors report R-squared I have to remind that this metric increases with the number of parameters and model complexity in general. This means that R-squared is not a fair metric to compare linear regression and the Random Forest regression. Since the authors aim to predict LOS another metric, such as the Root Mean Square of LOS prediction via the leave-one-out technique should be employed. Response: Thank you very much for your thoughtful feedback. We did indeed use cross-validation to determine the best-performing model. The R2 value was included solely for in-sample comparison, as it may be of interest to readers wishing to understand the proportion of variation explained by the model, specifically for the linear regression. However, the final model selection was based on the Root Mean Square Error (RMSE) derived from a 3-fold cross-validation process to ensure a rigorous assessment of model performance. To further clarify this point, we have updated the first paragraph of Section 3.2 accordingly. 2. The difference between states is not addressed. Mixed model with random intercepts that represent the state-specific LOS is easy to apply, and I recommend employing this model. I wonder how the state-specific LOS can be accounted using the ML methodology. Response: Thank you for your insightful feedback and suggestions. As illustrated in Figure 3, Table 1, and also elaborated in the Discussion section of the paper, there were no distinctive differences among states in terms of age and sex distributions or the average length of stay (LOS). This findings is also shown in Table 2 where the effect of state/territories was not statistically significant at the 1% level. Regarding the use of machine learning models, the state/territories variable has been incorporated as a categorical feature. The results aligned closely with our expectations and the linear regression outcomes, showing contribution of state/territories importance remains minimal and close to zero. The nonlinearity of LOS versus Age is not addressed in regression analysis. I suggest showing the cross plot (Age,LOS) and displaying linear, quadratic, and a nonparametric regression (such as ‘loess’ in R) to understand how nonlinear the relationship is. This univariate analysis must be repeated for all other predictors. Response: Thank you for your thoughtful feedback and constructive suggestions. In the revised manuscript, we have now included figures depicting the average length of stay (LOS) versus year, age, Activities of daily living (ADL), and Behavioral (BEH) need scores in the revised paper (please refer to Figures 6-8). The curves presented in these figures utilize the LOWESS smoothing method, as you recommended, and suggest potential non-linearity in these relationships. To address this observation more rigorously, we have updated the linear regression model by incorporating natural splines for year, age, ADL, and BEH scores. This allows for greater flexibility in capturing non-linear patterns while maintaining interpretability. Please note that the remaining variables in the model are categorical and have been modelled using indicator variables. The differences in average LOS across the levels of these categorical variables are already presented in Table 1, making additional graphical representation redundant for this purpose. Furthermore, Table 2 has been updated to reflect the results from the revised model. For the four continuous variables mentioned, significance tests have been provided for the overall effect of the spline terms. However, the coefficients for individual spline components are not reported, as they lack straightforward interpretability. 3. The authors must explain/interpret ‘Importance’ of the variables in Figs 4 and 5. Importance with respect to what? How to explain Importance to Medical Doctors? Why Importance scale is different in the figures? Response: We have clarified in the revised paper that the term "importance" presented in Figures 4 and 5 were specifically referring to permutation importance, a widely used model-agnostic technique in machine learning for assessing the contribution of individual variables in predictive models. It measures the effect of shuffling a feature on the model’s performance specifically the process involves training the model on the original data, computing the performance metric (e.g., RMSE), and then permuting each feature one at a time. After permuting a feature, the performance metric is recomputed, and the importance of the feature is calculated as the difference between the original and permuted performance scores. The formula for the importance of a feature is: Importance of feature=Performance (original)−Performance (permuted) The larger the drop in performance after permuting a feature, the more important that feature is for the model's predictions. To make it clearer, we have added additional clarification regarding the concept of permutation importance in Section 3.1 in the revised manuscript to ensure greater clarity for readers. 4. Data are not available: “No - some restrictions will apply.” It’s unclear why de-identified data cannot be accessed. I have to remind the authors that the data availability is a major condition for research transparency and results reproducibility. Response: The dataset used in this study consists of individual records organized and maintained by the Australian Institute of Health and Welfare (AIHW). Access to this dataset is restricted and granted only to approved individuals to ensure privacy and data security. Interested users may contact the AIHW directly to inquire about obtaining access to the de-identified data under the appropriate permissions. Attachments Attachment Submitted filename: PLOS ResponsetoReviewer_9 Jan 25.docx https://doi.org/10.1371/journal.pone.0321612.r003
23 Feb 2025 Decision Letter - Eugene Demidenko, Editor PONE-D-24-53339R1Expected Length of Stay in Residential Aged Care Facilities in Australia: Assessing the Impact of Dementia using Machine LearningPLOS ONE Dear Dr. Lee, 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. New graphs in Fig. 6-8 reveal a problem with this work. 1. Why in Fig. 6 Y=Average LOS has discrete values and in Figs 7-8 close to continuous?2. Variables ADL Score and BEH Score have a weak relationship with the Y variable. It looks like the regression line is horizontal. The Y and the BEH score can be modeled as a stepwise function. All other variables presented in Fig. 6 are not significant. 3. As far as the modeling of Y is concerned, the yellow line (a spline?) is the answer with R-squared hard to beat. I suspect that a quadratic function of Year on the log scale of Y can be as good as the yellow line. In summary, it looks like ML is a wrong approach to modeling Average LOS. Please submit your revised manuscript by Apr 09 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'. 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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, Eugene Demidenko, Ph.D. Academic Editor PLOS ONE [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 #1: All comments have been addressed ******** 2. 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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 compared linear regression and machine learning for predicting the length of stay (LOS) in residential aged care facilities. They identified dementia as an important factor and confirmed that machine learning outperformed linear regression in prediction accuracy. While this is an interesting study, several flaws should be addressed. Abstract The authors should present data rather than conclusions in the results section. The statement: “On average, residents with dementia stayed 6 months longer compared to those without, which suggests a positive association between LOS and levels of complex health care (CHC).” is inappropriate for the results section. Instead, present the proportion of residents with dementia staying less than and more than six months, along with the corresponding p-value. The claim: “Owing to the presence of heteroscedasticity and non-normality of LOS, the application of linear regression is likely to skew the estimates. Our study showed that among the different ML techniques, GBDT provided better predictive performance as it could capture nonlinear relationships and was less sensitive to changes or dependent on stringent assumptions compared with traditional linear regression.” should be supported with specific results. Present the performance metrics of different models, such as AUC for categorical outcomes and R² for continuous outcomes (e.g., DOI: 10.1038/s41598-024-82615-0). Introduction The authors extensively discuss the significance of aged care. However, given that the study focuses on machine learning for LOS prediction, more discussion on AI techniques is warranted. Consider incorporating relevant references (e.g., DOI: 10.69854/jcq.2024.0011). Methods Cite relevant references for the machine learning models used, such as Random Forest, Linear Regression, and Gradient Boosting. Move the Training and Validation section after the introduction of models for better flow. Results Major findings should be presented with specific data points, allowing readers to understand the results more clearly. Discussion The discussion section should be separate from the results section and structured as 4.0 Discussion to improve readability and clarity. ****** 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. https://doi.org/10.1371/journal.pone.0321612.r004
Revision 2
28 Feb 2025 Author Response Overall response: We thank the reviewer for the comments, and we apologise for the misleading conclusions that were actually drawn from our figures in the previous submission. The purpose of presenting Figures 6-8 in the earlier manuscript was to illustrate the relationship between LOS (Y variable) and explanatory variables (i.e., Year, BEH and ADL scores). Due to our large sample size (n=509,142), we realise that it is not efficient or possible to show the complete scatter plot with the full dataset. As such, in the previously figures, we plotted an average LOS for each unique value of an explanatory variable. The LOWESS curve was applied to the resulting average LOS shown in each figure, and these curves are not necessarily consistent with that could be obtained using the complete dataset. We have now clarified and reproduced our four plots in the current revised manuscript. In each plot, the LOWESS curve is obtained using the complete dataset, and the scatter points consist of 5,000 randomly selected subsample from the complete dataset. These scatter points should only be used as a snapshot of the full sample, and the non-linear patterns shown by the LOWESS curves are more important. In these new figures, it is clear that each of the four continuous variables may non-linearly influence the LOS. The paper has been revised to highlight these updates. In addition, we would like to restate our logic and steps of our analyses: 1. Provide plots of LOS vs explanatory variables to preliminarily analyse the potential relationships. 2. The observed non-linear relationships preliminarily support the potential usefulness of ML models, which can accommodate such non-linearity without having to specify it. In comparison, the four continuous variables are modelled via splines in the usual linear regression to accommodate non-linearity. 3. Both linear regression and ML models support the significance of ADL and BEH scores. 4. ML models outperforms the linear regression as evidenced by RMSE and (pseudo) R-square, and thus is recommended as the final models to study and project LOS in our present study. We believe that these new results can effectively address the previous concerns. Therefore, the three comments may no longer be applicable to our revised paper. Nevertheless, we decided to provide point-to-point response for the completeness. 1. Why in Fig. 6 Y=Average LOS has discrete values and in Figs 7-8 close to continuous? Response: Thank you for your comments. In these figures, we plotted the average LOS for each unique value of the corresponding explanatory variables. Discrete values were found for Year since there were only 13 distinct values. In contrast, the other explanatory variables (ADL and BEH scores in Figures 7 and 8, respectively) had many more unique values and thus appeared to be continuous data. 2. Variables ADL Score and BEH Score have a weak relationship with the Y variable. It looks like the regression line is horizontal. The Y and the BEH score can be modeled as a stepwise function. All other variables presented in Fig. 6 are not significant. Response: We thank the reviewer for highlighting this. As outlined above, these figures showed the LOWESS curve of unique values of each explanatory variable against the corresponding average LOS. They do not represent the true curve that may be obtained using the full sample. To more systematically justify the significance, the p-value in linear regression and importance scores in ML models should be used instead. In both cases, the ADL Score and BEH Score are believed to be significant and/or among the most important explanatory variables. 3. As far as the modeling of Y is concerned, the yellow line (a spline?) is the answer with R-squared hard to beat. I suspect that a quadratic function of Year on the log scale of Y can be as good as the yellow line. Response: Thank you for your comment. While a quadratic model might seem to capture the non-linearity observed in the scatter plot, we chose to use splines in linear regression because it is more general and flexible. Splines allow the relationship to vary across different ranges of the explanatory variable, without imposing the fixed shape that a quadratic function does. This flexibility is especially useful if the true relationship deviates from a simple quadratic form, especially when other explanatory variables are later included in the full model. Additionally, the ML models employed are designed to capture complex non-linear patterns without the need to specify a particular functional form in advance. Their ability to learn directly from the data means that they can adapt to various underlying relationships, including but not limited to linear, quadratic and other patterns. In other words, our approaches (spline in linear regression and ML models) minimize the risk of model misspecification and allow for a better fit if the true relationship is more complex than a simple quadratic. More importantly, as evidenced by (pseudo) R-square and RMSE, ML models outperform the linear regression. This validates the reason that they are chosen as the final model in this paper. Attachments Attachment Submitted filename: PLOS _ D 24_53339R1 ResponseReviewer_25Feb25.docx https://doi.org/10.1371/journal.pone.0321612.r005
9 Mar 2025 Decision Letter - Eugene Demidenko, Editor Expected Length of Stay in Residential Aged Care Facilities in Australia: Assessing the Impact of Dementia using Machine Learning PONE-D-24-53339R2 Dear Dr. Lee, 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. As a comment regarding your graphs 1a-1d, I suggest showing vertical kernel densities of Days's distribution. You can adopt Figures 8 and 9 from my book Demidenko E (2020). Advanced Statistics with Applications in R on page 677 and 678. Kind regards, Eugene Demidenko, Ph.D. Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0321612.r006
Formally Accepted
Acceptance Letter - Eugene Demidenko, Editor PONE-D-24-53339R2 PLOS ONE Dear Dr. Lee, 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 You will receive further instructions from the production team, including instructions on how to review your proof when it is ready. Please keep in mind that we are working through a large volume of accepted articles, so please give us a few days 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. Eugene Demidenko Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0321612.r007

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