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3/6/2025 2:03:48 PM | Browse: 66 | Download: 112
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Received |
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2024-09-29 09:04 |
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Peer-Review Started |
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2024-10-08 00:54 |
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To Make the First Decision |
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Return for Revision |
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2024-11-01 11:40 |
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Revised |
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2024-11-03 09:43 |
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Second Decision |
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2024-11-19 02:36 |
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Accepted by Journal Editor-in-Chief |
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Accepted by Executive Editor-in-Chief |
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2024-11-19 08:17 |
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Articles in Press |
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2024-11-19 08:17 |
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Publication Fee Transferred |
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Edit the Manuscript by Language Editor |
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2024-11-25 15:57 |
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Typeset the Manuscript |
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2024-12-04 01:57 |
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Publish the Manuscript Online |
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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
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Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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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
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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 |
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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 |
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