سامانه مدیریت نشریات علمی دانشگاه علوم پزشکی مشهد

Izadi, Abbas, Bakhshali, Mohamad Amin, Ghasemifard, Hadi, Sarrafzadeh, Omid. (1402). An Intelligent System for Management of Medical Equipment Maintenance. , 11(3), 161-167. doi: 10.22038/psj.2023.74922.1407
Abbas Izadi; Mohamad Amin Bakhshali; Hadi Ghasemifard; Omid Sarrafzadeh. "An Intelligent System for Management of Medical Equipment Maintenance". , 11, 3, 1402, 161-167. doi: 10.22038/psj.2023.74922.1407
Izadi, Abbas, Bakhshali, Mohamad Amin, Ghasemifard, Hadi, Sarrafzadeh, Omid. (1402). 'An Intelligent System for Management of Medical Equipment Maintenance', , 11(3), pp. 161-167. doi: 10.22038/psj.2023.74922.1407
Izadi, Abbas, Bakhshali, Mohamad Amin, Ghasemifard, Hadi, Sarrafzadeh, Omid. An Intelligent System for Management of Medical Equipment Maintenance. , 1402; 11(3): 161-167. doi: 10.22038/psj.2023.74922.1407

An Intelligent System for Management of Medical Equipment Maintenance

Journal of Patient Safety & Quality Improvement
مقاله 3، دوره 11، شماره 3، مهر 2023، صفحه 161-167    اصل مقاله (592.53 K)
نوع مقاله: Original Article
شناسه دیجیتال (DOI): 10.22038/psj.2023.74922.1407
نویسندگان
Abbas Izadi1؛ Mohamad Amin Bakhshali* 2؛ Hadi Ghasemifard1؛ 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 proposes an intelligent system for managing medical equipment maintenance in healthcare facilities. The system utilizes machine learning algorithms and data analytics to predict equipment failures, schedule maintenance tasks, and manage spare parts inventory efficiently. The aim is to improve equipment availability and reliability, reduce maintenance costs, and increase patient safety.
Materials and Methods:
The proposed system consists of several modules: data collection, preprocessing, equipment failure prediction, maintenance scheduling, spare parts inventory management, and integration. Real-world data is used to evaluate and compare the system's performance with other maintenance management approaches.
 
Results:
The results demonstrate that the proposed system can accurately predict equipment failures, schedule maintenance tasks efficiently, and manage spare parts inventory effectively. This improves equipment availability and reliability, reduces maintenance costs, and ensures that spare parts are available when needed without incurring excessive inventory costs.
Conclusion:
Overall, the proposed intelligent system for managing medical equipment maintenance is an effective solution for healthcare facilities to optimize maintenance operations, reduce costs, and ensure patient safety.
کلیدواژه‌ها
Medical E1quipment؛ Maintenance Management؛ Intelligent System؛ Machine Learning
مراجع
  1. Agarwal, R., & Sankar, C. (2018). A review on machine learning techniques for predictive maintenance. Procedia Computer Science, 132, 1411-1420.
  2. Azadeh, A., Ghaderi, H., & Moghaddam, M. (2017). A hybrid model based on artificial neural networks and imperialist competitive algorithm for predictive maintenance scheduling of medical equipment. Journal of Medical Systems, 41(8), 123.
  3. Bello-Orgaz, G., Jung, J. J., & Camacho, D. (2018). Social big data: Recent achievements and new challenges. Information Fusion, 42, 158-174.
  4. Li, Y., Yang, X., & Gao, R. X. (2018). Anomaly detection in machine health monitoring: A probabilistic approach. Mechanical Systems and Signal Processing, 103, 12-23.
  5. Montesinos, J. J., & Baiden, R. N. (2018). Predictive maintenance in industry 4.0: A review. IEEE Access, 6, 69044-69054.
  6. Sharma, S., & Mishra, P. (2019). Machine learning based predictive maintenance: A review. Engineering Science and Technology, an International Journal, 22(5), 1485-1499.
  7. Shen, C., Zhang, C., Guo, X., & Chen, C. (2018). A hybrid intelligent maintenance decision-making system for medical equipment. Journal of Intelligent & Fuzzy Systems, 34(2), 1199-1212.
  8. Sundar, S., Palanisamy, P., & Perumal, M. (2020). An intelligent predictive maintenance system using machine learning and internet of things. International Journal of Advanced Intelligence Paradigms, 15(3-4), 223-236.
  9. Wang, B., Zhang, Y., Li, Y., Li, X., Li, Y., & Li, C. (2020). Deep learning for predictive maintenance of industrial equipment: A review. IEEE Transactions on Industrial Informatics, 16(8), 5337-5350.
  10. Zhang, X., & Natarajan, S. (2019). Predictive maintenance of industrial systems using machine learning: A review. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 233(3), 247-258.
  11. Zamzam, A., Hasikin, K., & Abdul Wahab, A. (2023). Integrated failure analysis using machine learning predictive system for smart management of medical equipment maintenance. Engineering Applications of Artificial Intelligence, 125, 106715.
  12. Wang, X., Liu, M., Liu, C., Ling, L., & Zhang, X. (2023). Data-driven and Knowledge-based predictive maintenance method for industrial robots for the production stability of intelligent manufacturing. Expert Systems with Applications, 234, 121136.
  13. Qiu, X., Wang, J., Wang, D., & Yin, Y. (2023). Service-oriented multi-skilled technician routing and scheduling problem for medical equipment maintenance with sudden breakdown. Advanced Engineering Informatics, 57, 102090.
  14. Li, J., Han, D., Wu, Z., Wang, J., Li, K., & Castiglione, A. (2023). A novel system for medical equipment supply chain traceability based on alliance chain and attribute and role access control. Future Generation Computer Systems, 142, 195-211.
  15. Ding, X., Zhang, Y., Li, J., Mao, B., Guo, Y., & Li, G. (2023). A feasibility study of multi-mode intelligent fusion medical data transmission technology of industrial Internet of Things combined with medical Internet of Things. Internet of Things, 21, 100689.
آمار
تعداد مشاهده مقاله: 268
تعداد دریافت فایل اصل مقاله: 294