Title: Machine learning for predicting all-cause mortality of metabolic dysfunction-associated
fatty liver disease: a longitudinal study based on NHANES
Version: 1 Date: 17 Mar 2025
Reviewer's report:
This study has several strengths that enhance its significance and reliability. First, it leverages
a well-established, nationally representative dataset (NHANES III) with long-term follow-
up, ensuring a robust and comprehensive analysis of MAFLD-related mortality. Second,
by incorporating multiple machine learning models (Coxnet, RSF, GBS), the study allows
for a comparative evaluation of predictive performance, ensuring that the best-performing
model is identified. Third, the inclusion of diverse predictors—ranging from demographic and
behavioral factors to laboratory indicators—enhances the model’s ability to capture complex
risk relationships. Fourth, the use of interpretable machine learning methods, along with
traditional survival analyses, provides valuable clinical insights rather than treating the model
as a black box. Finally, the study’s findings offer practical implications for community health
interventions by identifying modifiable risk factors, which could improve patient outcomes and
guide personalized treatment strategies.
However, there are some limitations to consider. First, while machine learning models
demonstrated strong predictive capabilities, their real-world applicability depends on external
validation in independent cohorts, which was not performed in this study. Second, the reliance on
NHANES III data, although valuable, may not fully capture recent shifts in MAFLD diagnosis,
management, or treatment strategies. Third, despite careful feature selection, unmeasured
confounders such as genetic predisposition, medication use, and gut microbiota composition
may still influence all-cause mortality in MAFLD patients. Fourth, while the study establishes
associations between risk factors and mortality, causality cannot be inferred due to the
observational nature of the data. Lastly, the predictive models require further calibration and
refinement before they can be seamlessly integrated into clinical practice, highlighting the need
for additional prospective studies to confirm their utility.
Consider some or all of these articles to help with weaknesses identified in this review, focusing
on the methods:
https://onlinelibrary.wiley.com/doi/full/10.1111/jch.14745
https://www.elsevier.es/en-revista-annals-hepatology-16-avance-resumen-accurate-prediction-
all-cause-mortality-in-S1665268124003223
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0281922
https://www.nature.com/articles/s41514-023-00127-z
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0282622
https://pmc.ncbi.nlm.nih.gov/articles/PMC9099391/
https://www.sciencedirect.com/science/article/pii/S2475299123212512
Title: Machine learning for predicting all-cause mortality of metabolic dysfunction-associated
fatty liver disease: a longitudinal study based on NHANES
Version: 1 Date: 17 Mar 2025
Reviewer's report:
This study has several strengths that enhance its significance and reliability. First, it leverages
a well-established, nationally representative dataset (NHANES III) with long-term follow-
up, ensuring a robust and comprehensive analysis of MAFLD-related mortality. Second,
by incorporating multiple machine learning models (Coxnet, RSF, GBS), the study allows
for a comparative evaluation of predictive performance, ensuring that the best-performing
model is identified. Third, the inclusion of diverse predictors—ranging from demographic and
behavioral factors to laboratory indicators—enhances the model’s ability to capture complex
risk relationships. Fourth, the use of interpretable machine learning methods, along with
traditional survival analyses, provides valuable clinical insights rather than treating the model
as a black box. Finally, the study’s findings offer practical implications for community health
interventions by identifying modifiable risk factors, which could improve patient outcomes and
guide personalized treatment strategies.
However, there are some limitations to consider. First, while machine learning models
demonstrated strong predictive capabilities, their real-world applicability depends on external
validation in independent cohorts, which was not performed in this study. Second, the reliance on
NHANES III data, although valuable, may not fully capture recent shifts in MAFLD diagnosis,
management, or treatment strategies. Third, despite careful feature selection, unmeasured
confounders such as genetic predisposition, medication use, and gut microbiota composition
may still influence all-cause mortality in MAFLD patients. Fourth, while the study establishes
associations between risk factors and mortality, causality cannot be inferred due to the
observational nature of the data. Lastly, the predictive models require further calibration and
refinement before they can be seamlessly integrated into clinical practice, highlighting the need
for additional prospective studies to confirm their utility.
Consider some or all of these articles to help with weaknesses identified in this review, focusing
on the methods:
https://onlinelibrary.wiley.com/doi/full/10.1111/jch.14745
https://www.elsevier.es/en-revista-annals-hepatology-16-avance-resumen-accurate-prediction-
all-cause-mortality-in-S1665268124003223
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0281922
https://www.nature.com/articles/s41514-023-00127-z
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0282622
https://pmc.ncbi.nlm.nih.gov/articles/PMC9099391/
https://www.sciencedirect.com/science/article/pii/S2475299123212512
Please review this paper.
user
Title: Machine learning for predicting all-cause mortality of metabolic dysfunction-associated
fatty liver disease: a longitudinal study based on NHANES
Version: 1 Date: 17 Mar 2025
Reviewer's report:
This study has several strengths that enhance its significance and reliability. First, it leverages
a well-established, nationally representative dataset (NHANES III) with long-term follow-
up, ensuring a robust and comprehensive analysis of MAFLD-related mortality. Second,
by incorporating multiple machine learning models (Coxnet, RSF, GBS), the study allows
for a comparative evaluation of predictive performance, ensuring that the best-performing
model is identified. Third, the inclusion of diverse predictors—ranging from demographic and
behavioral factors to laboratory indicators—enhances the model’s ability to capture complex
risk relationships. Fourth, the use of interpretable machine learning methods, along with
traditional survival analyses, provides valuable clinical insights rather than treating the model
as a black box. Finally, the study’s findings offer practical implications for community health
interventions by identifying modifiable risk factors, which could improve patient outcomes and
guide personalized treatment strategies.
However, there are some limitations to consider. First, while machine learning models
demonstrated strong predictive capabilities, their real-world applicability depends on external
validation in independent cohorts, which was not performed in this study. Second, the reliance on
NHANES III data, although valuable, may not fully capture recent shifts in MAFLD diagnosis,
management, or treatment strategies. Third, despite careful feature selection, unmeasured
confounders such as genetic predisposition, medication use, and gut microbiota composition
may still influence all-cause mortality in MAFLD patients. Fourth, while the study establishes
associations between risk factors and mortality, causality cannot be inferred due to the
observational nature of the data. Lastly, the predictive models require further calibration and
refinement before they can be seamlessly integrated into clinical practice, highlighting the need
for additional prospective studies to confirm their utility.
Consider some or all of these articles to help with weaknesses identified in this review, focusing
on the methods:
https://onlinelibrary.wiley.com/doi/full/10.1111/jch.14745
https://www.elsevier.es/en-revista-annals-hepatology-16-avance-resumen-accurate-prediction-
all-cause-mortality-in-S1665268124003223
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0281922
https://www.nature.com/articles/s41514-023-00127-z
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0282622
https://pmc.ncbi.nlm.nih.gov/articles/PMC9099391/
https://www.sciencedirect.com/science/article/pii/S2475299123212512
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