Title: Leveraging Machine Learning for Precision Medicine: A Predictive Model for Cognitive
Impairment in Cholestasis Patients
Version: 3 Date: 19 Jan 2025
Reviewer's report:
The paper addresses a critical gap by creating a machine learning model tailored to predict
cognitive impairment in cholestasis patients. This focus on a high-risk yet understudied
population is commendable and offers potential for significant clinical impact. The authors
employ advanced techniques such as recursive feature elimination, SHAP analysis, and decision
curve analysis (DCA), showcasing a well-rounded and rigorous approach. The use of LightGBM
as the primary model balances performance and interpretability effectively.
The detailed SHAP analysis provides valuable insights into feature importance and interactions,
enhancing the clinical utility of the model. The exploration of non-linear effects and feature
interactions (e.g., age and plasma D-dimer levels) demonstrates a nuanced understanding of the
data.
The paper highlights the robust generalization of the LightGBM model, achieving an AUC of
0.7955 on the testing dataset, which supports its utility in real-world scenarios. The comparison
with other models, including random forest and stacking ensembles, strengthens the credibility
of the findings. However there are many places to improve.
1. While recursive feature elimination is mentioned, the criteria and rationale for final feature
selection could be elaborated. This would help readers understand why certain variables, like
age and plasma D-dimer, emerged as dominant predictors.
2.The paper does not extensively discuss the demographic and clinical diversity of the dataset
from Qingyang People’s Hospital. Providing details on potential biases in the dataset or the
generalizability of the findings to other populations would be beneficial.
3. Although LightGBM demonstrates strong generalization, its AUC in the testing dataset
(0.7955) is significantly lower than in the training dataset (0.8441). Addressing strategies to
further close this gap and avoid potential overfitting could strengthen the conclusions. How
about other classification model? compare them
4. While DCA is mentioned as evidence for clinical utility, the paper does not present
quantitative metrics or visualizations to support this claim. Including such details would
substantiate the model's applicability in clinical decision-making.
5. Expanding validation to external datasets across different clinical settings would enhance
confidence in the model's generalizability.
Title: Leveraging Machine Learning for Precision Medicine: A Predictive Model for Cognitive
Impairment in Cholestasis Patients
Version: 3 Date: 19 Jan 2025
Reviewer's report:
The paper addresses a critical gap by creating a machine learning model tailored to predict
cognitive impairment in cholestasis patients. This focus on a high-risk yet understudied
population is commendable and offers potential for significant clinical impact. The authors
employ advanced techniques such as recursive feature elimination, SHAP analysis, and decision
curve analysis (DCA), showcasing a well-rounded and rigorous approach. The use of LightGBM
as the primary model balances performance and interpretability effectively.
The detailed SHAP analysis provides valuable insights into feature importance and interactions,
enhancing the clinical utility of the model. The exploration of non-linear effects and feature
interactions (e.g., age and plasma D-dimer levels) demonstrates a nuanced understanding of the
data.
The paper highlights the robust generalization of the LightGBM model, achieving an AUC of
0.7955 on the testing dataset, which supports its utility in real-world scenarios. The comparison
with other models, including random forest and stacking ensembles, strengthens the credibility
of the findings. However there are many places to improve.
1. While recursive feature elimination is mentioned, the criteria and rationale for final feature
selection could be elaborated. This would help readers understand why certain variables, like
age and plasma D-dimer, emerged as dominant predictors.
2.The paper does not extensively discuss the demographic and clinical diversity of the dataset
from Qingyang People’s Hospital. Providing details on potential biases in the dataset or the
generalizability of the findings to other populations would be beneficial.
3. Although LightGBM demonstrates strong generalization, its AUC in the testing dataset
(0.7955) is significantly lower than in the training dataset (0.8441). Addressing strategies to
further close this gap and avoid potential overfitting could strengthen the conclusions. How
about other classification model? compare them
4. While DCA is mentioned as evidence for clinical utility, the paper does not present
quantitative metrics or visualizations to support this claim. Including such details would
substantiate the model's applicability in clinical decision-making.
5. Expanding validation to external datasets across different clinical settings would enhance
confidence in the model's generalizability.
Please review this paper.
user
Title: Leveraging Machine Learning for Precision Medicine: A Predictive Model for Cognitive
Impairment in Cholestasis Patients
Version: 3 Date: 19 Jan 2025
Reviewer's report:
The paper addresses a critical gap by creating a machine learning model tailored to predict
cognitive impairment in cholestasis patients. This focus on a high-risk yet understudied
population is commendable and offers potential for significant clinical impact. The authors
employ advanced techniques such as recursive feature elimination, SHAP analysis, and decision
curve analysis (DCA), showcasing a well-rounded and rigorous approach. The use of LightGBM
as the primary model balances performance and interpretability effectively.
The detailed SHAP analysis provides valuable insights into feature importance and interactions,
enhancing the clinical utility of the model. The exploration of non-linear effects and feature
interactions (e.g., age and plasma D-dimer levels) demonstrates a nuanced understanding of the
data.
The paper highlights the robust generalization of the LightGBM model, achieving an AUC of
0.7955 on the testing dataset, which supports its utility in real-world scenarios. The comparison
with other models, including random forest and stacking ensembles, strengthens the credibility
of the findings. However there are many places to improve.
1. While recursive feature elimination is mentioned, the criteria and rationale for final feature
selection could be elaborated. This would help readers understand why certain variables, like
age and plasma D-dimer, emerged as dominant predictors.
2.The paper does not extensively discuss the demographic and clinical diversity of the dataset
from Qingyang People’s Hospital. Providing details on potential biases in the dataset or the
generalizability of the findings to other populations would be beneficial.
3. Although LightGBM demonstrates strong generalization, its AUC in the testing dataset
(0.7955) is significantly lower than in the training dataset (0.8441). Addressing strategies to
further close this gap and avoid potential overfitting could strengthen the conclusions. How
about other classification model? compare them
4. While DCA is mentioned as evidence for clinical utility, the paper does not present
quantitative metrics or visualizations to support this claim. Including such details would
substantiate the model's applicability in clinical decision-making.
5. Expanding validation to external datasets across different clinical settings would enhance
confidence in the model's generalizability.
assistant