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Articles Published Processes
8/27/2026 8:00:26 AM | Browse: 2 | Download: 1
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Received |
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2026-03-02 03:32 |
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Peer-Review Started |
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2026-03-02 03:34 |
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First Decision by Editorial Office Director |
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2026-04-03 06:38 |
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Return for Revision |
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2026-04-03 06:38 |
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Revised |
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2026-04-09 15:30 |
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Publication Fee Transferred |
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Second Decision by Editor |
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2026-06-02 02:42 |
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Second Decision by Editor-in-Chief |
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Final Decision by Editorial Office Director |
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2026-06-02 06:57 |
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Articles in Press |
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2026-06-02 06:57 |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2026-07-21 03:29 |
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Publish the Manuscript Online |
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2026-08-27 08:00 |
| 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: https://creativecommons.org/Licenses/by-nc/4.0/ |
| Copyright |
©Author(s) (or their employer(s)) 2026. No commercial re-use. See Permissions. Published by Baishideng Publishing Group Inc. |
| 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 |
Minireviews |
| Article Title |
Artificial intelligence for early sepsis detection and dynamic prognostication in onco-critical care
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| Manuscript Source |
Invited Manuscript |
| All Author List |
Prashant Sirohiya, Prateek Maurya, Sakshi Arora, Brajesh Kumar Ratre, Ram Singh and Balbir Kumar |
| ORCID |
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| Funding Agency and Grant Number |
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| Corresponding Author |
Prashant Sirohiya, Assistant Professor, Department of Onco-Anaesthesia and Palliative Medicine, National Cancer Institute (Jhajjar), All India Institute of Medical Sciences, Badsa, New Delhi 110029, Delhi, India. prashantsirohiya@aiims.edu |
| Key Words |
Onco-critical care; Sepsis; Artificial intelligence; Machine learning; Febrile neutropenia; Prognostication; Explainable artificial intelligence; Electronic health records |
| Core Tip |
Integrating artificial intelligence (AI) and machine learning into onco-critical care could enable earlier sepsis detection and better risk assessment in critically ill cancer patients. Traditional scoring systems like Sequential Organ Failure Assessment and Acute Physiology and Chronic Health Evaluation II may be limited due to physiological traits like immunosuppression and treatment side effects. Emerging AI models that use real-time health data, vital signs, and unstructured clinical info show promise. Still, most evidence comes from retrospective studies and general intensive care unit data, and their use in oncology settings needs more validation. Explainable AI aids interpretability and clinician trust but should supplement, not replace, clinical judgment. More studies are needed for widespread adoption. |
| Publish Date |
2026-08-27 08:00 |
| Citation |
Sirohiya P, Maurya P, Arora S, Ratre BK, Singh R, Kumar B. Artificial intelligence for early sepsis detection and dynamic prognostication in onco-critical care. World J Crit Care Med 2026; 15(3): 120560
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| URL |
https://www.wjgnet.com/2220-3141/full/v15/i3/120560.htm |
| DOI |
https://doi.org/10.5492/wjccm.120560 |
Copyright © 1993-2026 Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA. All rights reserved, including rights relating to text and data mining, AI training, and similar technologies. For open-access content, the applicable copyright and licensing terms govern.
