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3/6/2025 2:03:48 PM | Browse: 66 | Download: 112
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)
Open Access This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Copyright The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
Article Reprints For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
Permissions For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
Publisher Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
Website http://www.wjgnet.com
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