Editorial
Copyright ©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Clin Cases. Apr 16, 2025; 13(11): 100966
Published online Apr 16, 2025. doi: 10.12998/wjcc.v13.i11.100966
Predicting outcomes using neural networks in the intensive care unit
Gumpeny R Sridhar, Venkat Yarabati, Lakshmi Gumpeny
Gumpeny R Sridhar, Department of Endocrinology and Diabetes, Endocrine and Diabetes Centre, Visakhapatnam 530002, India
Venkat Yarabati, Chief Architect, Data and Insights, AGILISYS, London W127RZ, United Kingdom
Lakshmi Gumpeny, Department of Internal Medicine, Gayatri Vidya Parishad Institute of Healthcare and Medical Technology, Visakhapatnam 530048, India
Author contributions: Sridhar GR and Venkat Y designed the concept and contributed to the writing; Lakshmi G contributed to the writing and editing of the manuscript; all of the authors read and approved the final version of the manuscript to be published.
Conflict-of-interest statement: All authors declare no conflict of interest in publishing the manuscript.
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/
Corresponding author: Gumpeny R Sridhar, FRCP (Hon), MD, Consultant Physician-Scientist, Department of Endocrinology and Diabetes, Endocrine and Diabetes Centre, 15-12-15 Krishnanagar, Visakhapatnam 530002, India. grsridhar@hotmail.com
Received: August 31, 2024
Revised: November 21, 2024
Accepted: December 12, 2024
Published online: April 16, 2025
Processing time: 116 Days and 17.5 Hours
Core Tip

Core Tip: Healthcare workers in intensive care units undertake swift and critical decisions, based on physiological and clinical data recorded in digital form, leading to information overload. Neural network models and machine learning can analyse the dense information and can potentially aid in decision making by patient triage, preventing treatment errors and providing insights into possible outcomes. Practical, legal and ethical issues need to be addressed as with other areas of healthcare. But research and its quick translation strongly suggests its imminent incorporation into routine clinical workflow.