Advancing airway management for ventilation optimization in critical healthcare with cloud computing and deep learning

Abstract

Improving patient outcomes in critical care settings is significantly connected to effective ventilation control. This research introduces a new method for improving ventilation methods in critical healthcare utilizing a long short-term memory (LSTM) network hosted in the cloud. Ventilators, pulse oximeters, and capnography are just a few examples of medical equipment that input data into the system, which then uploads the data to the cloud for analysis. The LSTM network can learn from data patterns and correlations, drawing on respiratory parameters' time dynamics, to provide real-time suggestions and predictions for ventilation settings. The system aims to improve clinical results and reduce the risk of ventilator-induced lung damage by tailoring ventilation techniques according to each patient's requirements and by forecasting potential issues. Due to remote monitoring technology, medical professionals can quickly analyze their patient's conditions and act accordingly. The system allows for continuous improvement using iterative learning of more data and feedback. With the ability to optimize breathing and enhance patient care in critical healthcare situations, a hopeful development in airway management is needed.