Con D, van Langenberg DR, Vasudevan A. Deep learning vs conventional learning algorithms for clinical prediction in Crohn's disease: A proof-of-concept study. World J Gastroenterol 2021; 27(38): 6476-6488 [PMID: 34720536 DOI: 10.3748/wjg.v27.i38.6476]
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03722500
Submitted on:
October 18, 2021, 13:53
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Reader Comments:
This paper compares the differences between deep learning algorithms and traditional algorithms in predicting the efficacy of TNF-α for the treatment of Crohn's disease, and the results confirm that deep learning algorithms have stronger predictive power. The paper provides ideas for the clinical treatment of Crohn's disease, and clinicians can more accurately predict the effectiveness of patients when using TNF-α to treat them, thus being able to provide precise treatment to patients. The paper is well-written and is of reference value to clinicians.
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Reader's ID:
05230413
Submitted on:
October 15, 2021, 08:34
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Reader Comment Standards for Published Articles:
1 Title
Does the title reflect the main subject/hypothesis of the manuscript?
2 Abstract
Does the abstract summarize and reflect the work described in the manuscript?
3 Key Words
Do the key words reflect the focus of the manuscript?
4 Background
Does the manuscript adequately describe the background, present status and significance of the study?
5 Methods
Does the manuscript describe methods (e.g., experiments, data analysis, surveys, and clinical trials, etc.) in adequate detail?
6 Results
Are the research objectives achieved by the experiments used in this study?
Has the study made meaningful contributions towards research progress in this field?
7 Discussion
Does the manuscript interpret the findings adequately and appropriately, highlighting the key points concisely, clearly and logically?
Are the findings and their applicability/relevance to the literature stated in a clear and definite manner?
Is the Discussion accurate and does it discuss the paper’s scientific significance and/or relevance to clinical practice sufficiently?
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Does the author self-cite, omit, incorrectly cite and/or over-cite references?
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Are the style, language and grammar accurate and appropriate?
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Reader Comments:
Explained variation, which is Nagelkerke's R square in this binary classification, would be 8~15% based on your event rate and c-index. This low value suggests that underlying patterns in a given dataset are weakly related to the outcome event, when we consider the common finding that prognostic models usually have R2 around 20–30%. If your deep learning found a hidden underlying pattern in your dataset, the AUC should be expected to be more higher. I believe it is not enough to provide slightly higher AUC with statistical significance.
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