Abstract: The Automatic Ventilator is a controllable, automated add-on solution to the existing and widely available Bag Valve Mask. The device compresses the BVM with a mechanical system that is able to provide consistent and accurate ventilation with positive-pressure. This solution exists within the top range of high-acuity limited-operability (HALO) ventilator solutions with an a priori design to produce volume and pressure cycled ventilation that includes positive end-expiratory pressure (PEEP) and enriched oxygen sources. In this situation of COVID 19, many people are being exposed to corona virus, resulting in difficulty in breathing and a drop in oxygen percentage of blood. A mechanical ventilator is playing a vital role in tackling this situation but the ventilation process is neither readily available nor affordable. The idea behind this work is to propose a simplified design of a mechanical ventilator to reduce the cost and automate the Mechanical ventilation process. The simplified design, it's working, and required components are elaborated in this paper. The simulation of the proposed design is made in MATLAB/Simulink platform which is also discussed below. Simulation results are promising and precise which allows the study on ventilator model without jeopardizing the life of human subjects as in clinical approach and hides the complexity of computational models from the user. Furthermore, advancements in this model are done by the machine learning approach.: Successful weaning from prolonged mechanical ventilation (MV) is an important issue in respiratory care centers (RCCs. This study aims to utilize artificial intelligence algorithms to build predictive models for the successful timing of the weaning of patients from MV in RCCs and to implement a dashboard with the best model in RCC settings. A total of 670 incubated patients in the RCC in Chi Mei Medical Center were included in the study. Twenty-six feature variables were selected to build the predictive models with artificial intelligence (AI)/machine-learning (ML) algorithms. An interactive dashboard with the best model was developed and deployed. A preliminary impact analysis was then conducted. Our results showed that all seven predictive models had a high area under the receiver operating characteristic curve (AUC), which ranged from 0.792 to 0.868. The preliminary impact analysis revealed that the mean number of ventilator days required for the successful weaning of the patients was reduced by 0.5 after AI intervention. The development of an AI prediction dashboard is a promising method to assist in the prediction of the optimal timing of weaning from MV in RCC settings. However, a systematic prospective study of AI intervention is still needed.

Keywords: Ventilator, Bag Valve Mask, Sensors, Arduino Controller, IoT Module.

PDF | DOI: 10.17148/IJARCCE.2023.12343

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