سامانه مدیریت نشریات علمی دانشگاه علوم پزشکی مشهد

Izadi, Abbas, Ghasemifard, Hadi, Bakhshali, Mohamad Amin, Roudsarabi, Nadia, Sarrafzadeh, Omid. (1402). An Intelligent System for Medical Oxygen Consumption Management Using Oximetry and Barometry. , 11(3), 175-182. doi: 10.22038/psj.2023.74926.1408
Abbas Izadi; Hadi Ghasemifard; Mohamad Amin Bakhshali; Nadia Roudsarabi; Omid Sarrafzadeh. "An Intelligent System for Medical Oxygen Consumption Management Using Oximetry and Barometry". , 11, 3, 1402, 175-182. doi: 10.22038/psj.2023.74926.1408
Izadi, Abbas, Ghasemifard, Hadi, Bakhshali, Mohamad Amin, Roudsarabi, Nadia, Sarrafzadeh, Omid. (1402). 'An Intelligent System for Medical Oxygen Consumption Management Using Oximetry and Barometry', , 11(3), pp. 175-182. doi: 10.22038/psj.2023.74926.1408
Izadi, Abbas, Ghasemifard, Hadi, Bakhshali, Mohamad Amin, Roudsarabi, Nadia, Sarrafzadeh, Omid. An Intelligent System for Medical Oxygen Consumption Management Using Oximetry and Barometry. , 1402; 11(3): 175-182. doi: 10.22038/psj.2023.74926.1408

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

Journal of Patient Safety & Quality Improvement
مقاله 5، دوره 11، شماره 3، مهر 2023، صفحه 175-182    اصل مقاله (821.98 K)
نوع مقاله: Original Article
شناسه دیجیتال (DOI): 10.22038/psj.2023.74926.1408
نویسندگان
Abbas Izadi1؛ Hadi Ghasemifard1؛ Mohamad Amin Bakhshali* 2؛ Nadia Roudsarabi1؛ Omid Sarrafzadeh1
1Deputy of Treatment, Mashhad University of Medical Sciences, Mashhad, Iran
2Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
چکیده
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.
کلیدواژه‌ها
Medical Oxygen Consumption؛ Oximetry؛ Barometry؛ Decision Tree؛ Artificial neural network
مراجع
  1. Amato, F., López, A., Peña-Méndez, E. M., Vaňhara, P., & Hampl, A. (2013). Artificial neural networks in medical diagnosis. Journal of applied biomedicine, 11(2), 47-58.
  2. Baruah, H., & Barua, A. (2021). A review on Internet of Things based smart healthcare systems. IETE Technical Review, 38(3), 305-316.
  3. Chawla, N. V. (2005). Data mining for imbalanced datasets: An overview. Data mining and knowledge discovery handbook, 853-867.
  4. Géron, A. (2019). Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: Concepts, tools, and techniques to build intelligent systems. O'Reilly Media, Inc.
  5. Gomes, L. F. A. M., Alves, M. C., da Silva, M. E. B., & Souza, R. C. (2019). Internet of things and machine learning: applications and challenges in healthcare. In International Conference on Computer Supported Education (pp. 389-405). Springer, Cham.
  6. Hosseini, S. H., & Rezaei, S. (2016). A survey on applications of decision tree in medical science. Acta Informatica Medica, 24(5),364-369.
  7. Li, L., Li, Z., Liang, X., & Yang, J. (2020). A review on internet of things in healthcare: Applications, recent advances, and challenges. Journal of Healthcare Engineering, 2020, 1-22.
  8. Mohammadi, M., & Esmaeilpour, M. (2018). Developing and comparing two predictive models based on linear regression and decision trees for predicting the length of stay in the hospital after surgery. International Journal of Health Care Quality Assurance, 31(7), 645-654.
  9. Nair, V., & Hinton, G. E. (2010). Rectified linear units improve restricted boltzmann machines. In Proceedings of the 27th international conference on machine learning (ICML-10) (pp. 807-814).
  10. Ravi, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., & Yang, G. Z. (2017). Deep learning for health informatics. IEEE journal of biomedical and health informatics, 21(1), 4-21.
  11. Shaban, A., Abdelwahed, A., Gravio, G., Afefy, I., & Patriarca, R. (2022). A systems-theoretic hazard analysis for safety-critical medical gas pipeline and oxygen supply systems. Journal of Loss Prevention in the Process Industries, 77, 104782.
  12. Laal, F., Hanifi, S., Madvari, R., Khoshakhlagh, A., & Arefi, M. (2023). Providing an approach to analyze the risk of central oxygen tanks in hospitals during the COVID-19 pandemic. Heliyon, 9(8), e18736.
  13. Wang, J., Chen, M., Chen, Q., & Wang, H. (2022). Medical Oxygen Sensor Based on Acoustic Resonance Frequency Tracking Using FPGA. IEEE Sensors Journal, 22(21), 21281-21286.
  14. Botney, R., Answine, J., & Cowles, C. (2020). Failures of the Oxygen Supply. Anesthesiology Clinics, 38(4), 901-921.
  15. Lu, J., Ling, K., Zhong, W., He, H., Ruan, Z., & Han, W. (2023). Construction of a 5G-based, three-dimensional, and efficiently connected emergency medical management system. Heliyon, 9(3), e13826.
آمار
تعداد مشاهده مقاله: 200
تعداد دریافت فایل اصل مقاله: 156