Publication Name	World Journal of Methodology
Manuscript ID	101837
Country	Viet Nam

Received	2024-09-29 09:04
Peer-Review Started	2024-10-08 00:54
To Make the First Decision
Return for Revision	2024-11-01 11:40
Revised	2024-11-03 09:43
Second Decision	2024-11-19 02:36
Accepted by Journal Editor-in-Chief
Accepted by Executive Editor-in-Chief	2024-11-19 08:17
Articles in Press	2024-11-19 08:17
Publication Fee Transferred
Edit the Manuscript by Language Editor	2024-11-25 15:57
Typeset the Manuscript	2024-12-04 01:57
Publish the Manuscript Online	2025-03-06 10:23

ISSN	2222-0682 (online)
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Category	Pediatrics
Manuscript Type	Retrospective Study
Article Title	Machine learning-based models for prediction of in-hospital mortality in patients with dengue shock syndrome
Manuscript Source	Invited Manuscript
All Author List	Luan Thanh Vo, Thien Vu, Thach Ngoc Pham, Tung Huu Trinh and Thanh Tat Nguyen
Funding Agency and Grant Number
Corresponding Author	Thanh Tat Nguyen, MD, PhD, Department of Tuberculosis and Epidemiology, Woolcock Institute of Medical Research, Pham Ngoc Thach Street, Ho Chi Minh City 700000, Viet Nam. thanhhonor@gmail.com
Key Words	Dengue shock syndrome; Dengue mortality; Machine learning; Supervised models; Logistic regression; Random forest; K-nearest neighbors; Support vector machine; Extreme Gradient Boost; Shapley addictive explanations
Core Tip	The in-hospital mortality rate of children with dengue shock syndrome (DSS) at a large tertiary pediatric hospital in Vietnam was 3%. The supervised models showed good predictive value. In particular, the random forest and Extreme Gradient Boost models demonstrated the highest model performance. The supervised machine learning model showed that the nine most important predictive variables included younger age, presence of underlying diseases, severe transaminitis, critical bleeding, platelet transfusion requirement, elevated international normalized ratio and blood lactate levels, and high vasoactive inotropic score (> 30). Identification of mortality predictors in patients with DSS will help optimize management protocols to enhance survival outcomes.
Publish Date	2025-03-06 10:23
Citation	<p>Vo LT, Vu T, Pham TN, Trinh TH, Nguyen TT. Machine learning-based models for prediction of in-hospital mortality in patients with dengue shock syndrome. <i>World J Methodol</i> 2025; 15(3): 101837</p>
URL	https://www.wjgnet.com/2222-0682/full/v15/i3/101837.htm
DOI	https://dx.doi.org/10.5662/wjm.v15.i3.101837

Full Article (PDF)	WJM-15-101837-with-cover.pdf
Manuscript File	101837_Auto_Edited_113649.docx
Answering Reviewers	101837-answering-reviewers.pdf
Audio Core Tip	101837-audio.mp3
Biostatistics Review Certificate	101837-biostatistics-statement.pdf
Conflict-of-Interest Disclosure Form	101837-conflict-of-interest-statement.pdf
Copyright License Agreement	101837-copyright-assignment.pdf
Signed Informed Consent Form(s) or Document(s)	101837-informed-consent-statement.pdf
Institutional Review Board Approval Form or Document	101837-institutional-review-board-statement.pdf
Non-Native Speakers of English Editing Certificate	101837-non-native-speakers.pdf
Supplementary Material	101837-supplementary-material.xlsx
Peer-review Report	101837-peer-reviews.pdf
Scientific Misconduct Check	101837-scientific-misconduct-check.png
Scientific Editor Work List	101837-scientific-editor-work-list.pdf
CrossCheck Report	101837-crosscheck-report.pdf