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Articles Published Processes
9/7/2022 3:41:07 AM | Browse: 318 | Download: 1025
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Received |
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2021-06-22 22:44 |
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Peer-Review Started |
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2021-06-22 22:47 |
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To Make the First Decision |
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Return for Revision |
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2021-07-31 03:56 |
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Revised |
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2021-08-13 16:32 |
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Second Decision |
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2022-07-04 03:05 |
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Accepted by Journal Editor-in-Chief |
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Accepted by Executive Editor-in-Chief |
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2022-07-06 04:54 |
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Articles in Press |
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2022-07-06 04:54 |
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Publication Fee Transferred |
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Edit the Manuscript by Language Editor |
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2022-06-09 01:06 |
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Typeset the Manuscript |
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2022-07-22 02:02 |
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Publish the Manuscript Online |
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2022-09-07 03:41 |
| ISSN |
2220-3141(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: http://creativecommons.org/Licenses/by-nc/4.0/ |
| Copyright |
© The Author(s) 2022. 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 |
Critical Care Medicine |
| Manuscript Type |
Retrospective Study |
| Article Title |
Prediction of hospital mortality in intensive care unit patients from clinical and laboratory data: A machine learning approach
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| Manuscript Source |
Invited Manuscript |
| All Author List |
Elena Caires Silveira, Soraya Mattos Pretti, Bruna Almeida Santos, Caio Fellipe Santos Corrêa, Leonardo Madureira Silva and Fabrício Freire de Melo |
| ORCID |
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| Funding Agency and Grant Number |
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| Corresponding Author |
Fabrício Freire de Melo, PhD, Professor, Multidisciplinary Institute of Health, Federal University of Bahia, Rua Hormindo Barros, 58, Quadra 17, Lote 58, Candeias, Vitória da Conquista 45-029094, Brazil. freiremeloufba@gmail.com |
| Key Words |
Hospital mortality; Machine learning; Patient outcome assessment; Routinely collected health data; Intensive care units; Critical care outcomes |
| Core Tip |
Considering the critical nature of patients admitted to intensive care units (ICUs), this study seeks to analyze clinical and laboratory data using a machine learning model based on a Random Forest algorithm. Consequently, we developed a binary classifier that forecasts death outcome, achieving a relevant area under the curve value of 0.85 and identifying the variables that contributed the most to the prediction. With this, we aim to contribute to the improvement and methodological advancement in the development of clinically relevant machine learning tools, seeking to make medical practice decisions more accurate and reduce mortality in ICU patients. |
| Publish Date |
2022-09-07 03:41 |
| Citation |
Caires Silveira E, Mattos Pretti S, Santos BA, Santos Corrêa CF, Madureira Silva L, Freire de Melo F. Prediction of hospital mortality in intensive care unit patients from clinical and laboratory data: A machine learning approach. World J Crit Care Med 2022; 11(5): 317-329 |
| URL |
https://www.wjgnet.com/2220-3141/full/v11/i5/317.htm |
| DOI |
https://dx.doi.org/10.5492/wjccm.v11.i5.317 |
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