An Intelligent System for Medical Oxygen Consumption Management Using Oximetry and Barometry

Introduction:This paper presents the development of an intelligent system for managing medical oxygen consumption using oximetry and barometry in the hospitals of Mashhad University of Medical Sciences, utilizing machine learning methods. The system integrates various sensors and machine learning algorithms to enable real-time monitoring and control of the oxygen supply chain. Materials and Methods: The proposed approach utilizes multiple sensors to measure the purity and pressure of medical oxygen, and this data is collected and processed using machine learning algorithms. The system uses a decision tree model to classify the purity and pressure readings and identify deviations from the specified parameters. The system also utilizes an artificial neural network model to predict future oxygen consumption levels, enabling proactive supply chain management. The system consists of two main components: the hardware component and the software component. The software component includes machine learning algorithms for data processing and system management. Results: The proposed system has been tested in several hospitals affiliated with Mashhad University of Medical Sciences, and the results show that it can effectively monitor and manage medical oxygen consumption with high accuracy and reliability. The machine learning algorithms used in the system have the potential to improve patient safety by identifying potential issues in the oxygen supply chain before they become critical.  Conclusion:In conclusion, this paper presents an innovative and intelligent system that utilizes machine learning methods to enhance the management of medical oxygen consumption in hospitals significantly.