Using Artificial Intelligence to Predict Mechanical Ventilation Weaning Success in Patients with Respiratory Failure, Including Those with Acute Respiratory Distress Syndrome

Stivi, Tamar and Padawer, Dan and Dirini, Noor and Nachshon, Akiva and Batzofin, Baruch M. and Ledot, Stephane (2024) Using Artificial Intelligence to Predict Mechanical Ventilation Weaning Success in Patients with Respiratory Failure, Including Those with Acute Respiratory Distress Syndrome. Journal of Clinical Medicine, 13 (5). p. 1505. ISSN 2077-0383

[thumbnail of jcm-13-01505.pdf] Text
jcm-13-01505.pdf - Published Version

Download (297kB)

Abstract

The management of mechanical ventilation (MV) remains a challenge in intensive care units (ICUs). The digitalization of healthcare and the implementation of artificial intelligence (AI) and machine learning (ML) has significantly influenced medical decision-making capabilities, potentially enhancing patient outcomes. Acute respiratory distress syndrome, an overwhelming inflammatory lung disease, is common in ICUs. Most patients require MV. Prolonged MV is associated with an increased length of stay, morbidity, and mortality. Shortening the MV duration has both clinical and economic benefits and emphasizes the need for better MV weaning management. AI and ML models can assist the physician in weaning patients from MV by providing predictive tools based on big data. Many ML models have been developed in recent years, dealing with this unmet need. Such models provide an important prediction regarding the success of the individual patient’s MV weaning. Some AI models have shown a notable impact on clinical outcomes. However, there are challenges in integrating AI models into clinical practice due to the unfamiliar nature of AI for many physicians and the complexity of some AI models. Our review explores the evolution of weaning methods up to and including AI and ML as weaning aids.

Item Type: Article
Subjects: Open Archive Press > Multidisciplinary
Depositing User: Unnamed user with email support@openarchivepress.com
Date Deposited: 06 Mar 2024 09:23
Last Modified: 06 Mar 2024 09:23
URI: http://library.2pressrelease.co.in/id/eprint/1874

Actions (login required)

View Item
View Item