Sridhar GR, Yarabati V, Gumpeny L. Predicting outcomes using neural networks in the intensive care unit. World J Clin Cases 2025; 13(11): 100966 [DOI: 10.12998/wjcc.v13.i11.100966]
Corresponding Author of This Article
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
Research Domain of This Article
Critical Care Medicine
Article-Type of This Article
Editorial
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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/
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
Abstract
Patients in intensive care units (ICUs) require rapid critical decision making. Modern ICUs are data rich, where information streams from diverse sources. Machine learning (ML) and neural networks (NN) can leverage the rich data for prognostication and clinical care. They can handle complex nonlinear relationships in medical data and have advantages over traditional predictive methods. A number of models are used: (1) Feedforward networks; and (2) Recurrent NN and convolutional NN to predict key outcomes such as mortality, length of stay in the ICU and the likelihood of complications. Current NN models exist in silos; their integration into clinical workflow requires greater transparency on data that are analyzed. Most models that are accurate enough for use in clinical care operate as ‘black-boxes’ in which the logic behind their decision making is opaque. Advances have occurred to see through the opacity and peer into the processing of the black-box. In the near future ML is positioned to help in clinical decision making far beyond what is currently possible. Transparency is the first step toward validation which is followed by clinical trust and adoption. In summary, NNs have the transformative ability to enhance predictive accuracy and improve patient management in ICUs. The concept should soon be turning into reality.
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.
