Title: Clinical Data Based XGBoost Algorithm for Infection Risk Prediction of Patients
with Decompensated Cirrhosis: A 10-year (2012-2021) Multicenter Retrospective Case-control
Study
Version: 2 Date: 04 Jul 2023
Reviewer's report:
In this study, the authors propose to appraise effective predictors for infection in patients with
decompensated cirrhosis (DC) by using the XGBoost algorithm in a retrospective case-control
study of 6,648 patients. This study may be interesting for researchers who are mainly interested
in decompensated cirrhosis. However, the paper needs additional work, and some minor changes
are recommended before considering the paper for publication.
• Before using nonparametric methods for statistical analysis, whether the data are normally
distributed should be checked. It should be stated with which statistical test was used to check
if the normality assumption of the data was not met.
• Please provide the details of the solutions for the preprocessing problems of the data (missing
data, imbalanced data, etc.)
• Which correlation method (Spearman or Pearson) was used in the data analysis? Which method
was used in the univariate analysis? Please provide the name of all the statistical tests in the
statistical methods section. Additionally, please give the formula details of the performance
criteria (AUC, PPV etc.)
Title: Clinical Data Based XGBoost Algorithm for Infection Risk Prediction of Patients
with Decompensated Cirrhosis: A 10-year (2012-2021) Multicenter Retrospective Case-control
Study
Version: 2 Date: 04 Jul 2023
Reviewer's report:
In this study, the authors propose to appraise effective predictors for infection in patients with
decompensated cirrhosis (DC) by using the XGBoost algorithm in a retrospective case-control
study of 6,648 patients. This study may be interesting for researchers who are mainly interested
in decompensated cirrhosis. However, the paper needs additional work, and some minor changes
are recommended before considering the paper for publication.
• Before using nonparametric methods for statistical analysis, whether the data are normally
distributed should be checked. It should be stated with which statistical test was used to check
if the normality assumption of the data was not met.
• Please provide the details of the solutions for the preprocessing problems of the data (missing
data, imbalanced data, etc.)
• Which correlation method (Spearman or Pearson) was used in the data analysis? Which method
was used in the univariate analysis? Please provide the name of all the statistical tests in the
statistical methods section. Additionally, please give the formula details of the performance
criteria (AUC, PPV etc.)
Please review this paper.
user
Title: Clinical Data Based XGBoost Algorithm for Infection Risk Prediction of Patients
with Decompensated Cirrhosis: A 10-year (2012-2021) Multicenter Retrospective Case-control
Study
Version: 2 Date: 04 Jul 2023
Reviewer's report:
In this study, the authors propose to appraise effective predictors for infection in patients with
decompensated cirrhosis (DC) by using the XGBoost algorithm in a retrospective case-control
study of 6,648 patients. This study may be interesting for researchers who are mainly interested
in decompensated cirrhosis. However, the paper needs additional work, and some minor changes
are recommended before considering the paper for publication.
• Before using nonparametric methods for statistical analysis, whether the data are normally
distributed should be checked. It should be stated with which statistical test was used to check
if the normality assumption of the data was not met.
• Please provide the details of the solutions for the preprocessing problems of the data (missing
data, imbalanced data, etc.)
• Which correlation method (Spearman or Pearson) was used in the data analysis? Which method
was used in the univariate analysis? Please provide the name of all the statistical tests in the
statistical methods section. Additionally, please give the formula details of the performance
criteria (AUC, PPV etc.)
assistant