Review for 10.1186/s12876-023-02753-z (2023)

Reviewer’s report
Title: 
Using machine learning methods to predict 28-day mortality in patients
with hepatic encephalopathy
Version: 
0 
Date: 
24 Sep 2022
Reviewer's report:
The authors conducted an analysis of a publicly available dataset by machine
learning algorithms to predict 28-day mortality in patients with hepatic
encephalopathy. Some comments about the study:
1. Inclusion and exclusion criteria determine the external validity and
generalizability of the study result. Is there a reason to exclude patients with
multiple ICU admission? Did the authors mean repeated hepatic encephalopathy
of the same patient? What are the baseline characteristics of excluded patients?
2. On page 4, line 10, the author used the subtitle "Predictors of severe AKI". I
wondered if the title were meant to be "Predictors of HE (hepatic
encephalopathy)"? Moreover, the criteria to select these predictors is better to be
clarified.
3. The authors use recursive feature elimination (RFE) to select features. Is the
method based on a support vector machine (Chen et al., IEEE Explore,
DOI:10.1109/ICMLA.2007.35)? Illustrate the detail of the method by adding
reference to help the readers to understand more. Although RFE fits the support
vector well, how did the authors infer that RFE selects the best features for
algorithms, including the neural networks (NNET), gradient boosting machine
(GBM), random forest (RF), and bagged trees (BT)?
4. In the Results section, it is essential to show the number of 28-day mortality,
which is the primary outcome. 
5. APSIII, SOFA, INR, TBIL, albumin, BUN, AKI and mechanical ventilation
were selected as the most influential features and thus the number of features
(variables) was reduced (i.e., no longer a high-dimension dataset). Logistic
regression, a frequently used model in biostatistics, can be applied to predict 28-
day mortality with a ROC curve and AUC. Could the authors try to compare the
"conventional" method to the aforementioned algorithms in terms of AUC?
6. What did "actual prediction" and "average prediction" mean in figure 4?
7. What does the higher positive model output mean on Page 7? Did it mean a
more accurate prediction?
8. In Figure 4, what did phi in x-axis means in the Shapley value? When
TBIL=9.7, the phi is approximately -0.017 phi; how to interpret the value? In
addition, were TBIL, BUN, and ALB continuous variables?
9. For the missing data, multiple imputations to generate the data were done.
However, multiple imputation methods usually hypothesize that the values were
missing by random, which may not be true in clinical practice. Did the multiple
imputations applied in this study assume missing by random? 
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Is the study design appropriate to answer the research question (including the use
of appropriate controls), and are the conclusions supported by the evidence
presented?
Yes
Are the methods sufficiently described to allow the study to be repeated?
Yes
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Yes
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Yes
Are the images in this manuscript (including electrophoretic gels and blots) free
from apparent manipulation?
Yes