Standard
Medical Asset Management: Deep Learning Based Asset Usage Prediction in a Hospital Setting Using Real Data. / Esmaeili, Mona; Akhavan, Zeinab
; Nasiri, Hamid et al.
Proceedings - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023. ed. / M. Arif Wani; Mihai Boicu; Moamar Sayed-Mouchaweh; Pedro Henriques Abreu; Joao Gama. Institute of Electrical and Electronics Engineers Inc., 2023. p. 944-951 (Proceedings - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023).
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Harvard
Esmaeili, M, Akhavan, Z
, Nasiri, H, Worku, YM, Arzo, ST, Stavropoulos, A, Devetsikiotis, M & Zarkesh-Ha, P 2023,
Medical Asset Management: Deep Learning Based Asset Usage Prediction in a Hospital Setting Using Real Data. in M Arif Wani, M Boicu, M Sayed-Mouchaweh, PH Abreu & J Gama (eds),
Proceedings - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023. Proceedings - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023, Institute of Electrical and Electronics Engineers Inc., pp. 944-951, 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023, Jacksonville, United States,
15/12/23.
https://doi.org/10.1109/ICMLA58977.2023.00140APA
Esmaeili, M., Akhavan, Z.
, Nasiri, H., Worku, Y. M., Arzo, S. T., Stavropoulos, A., Devetsikiotis, M., & Zarkesh-Ha, P. (2023).
Medical Asset Management: Deep Learning Based Asset Usage Prediction in a Hospital Setting Using Real Data. In M. Arif Wani, M. Boicu, M. Sayed-Mouchaweh, P. H. Abreu, & J. Gama (Eds.),
Proceedings - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023 (pp. 944-951). (Proceedings - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023). Institute of Electrical and Electronics Engineers Inc..
https://doi.org/10.1109/ICMLA58977.2023.00140Vancouver
Esmaeili M, Akhavan Z
, Nasiri H, Worku YM, Arzo ST, Stavropoulos A et al.
Medical Asset Management: Deep Learning Based Asset Usage Prediction in a Hospital Setting Using Real Data. In Arif Wani M, Boicu M, Sayed-Mouchaweh M, Abreu PH, Gama J, editors, Proceedings - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 944-951. (Proceedings - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023). doi: 10.1109/ICMLA58977.2023.00140
Author
Bibtex
@inproceedings{33846db39d464cbf8b9bd5325b10ac7b,
title = "Medical Asset Management: Deep Learning Based Asset Usage Prediction in a Hospital Setting Using Real Data",
abstract = "Periodic Automatic Replenishment (PAR) is an inventory management policy that assists the healthcare sector in keeping the right amount of stock on hand to avoid excess stock and the potential for products to expire. Traditionally, hospitals rely on the experience and firsthand knowledge of stock management technicians to keep their store supplies with enough equipment. However, manual management based on 'gut feeling' and/or nursing feedback may lead to missing products or incorrect stock orders. Extracting accurate data is often too complex or time-consuming, resulting in a lack of critical reporting data to manage product inventories across the organization properly. However, adopting forecasting techniques and incorporating them into traditional PAR management policies can provide efficient solutions for controlling hospital warehouse inventory at the lowest cost. We have proposed a deep learning-based framework to monitor inventories to reduce costs and variation, create efficiencies, and improve the quality of patient care in hospitals. Furthermore, the proposed forecasting framework's performance is assessed using a real-world scenario within a hospital. The findings indicate that the system is capable of accurately tracking the usage of medical equipment, which results in a significant reduction in the unavailability of assets.",
keywords = "deep learning, hospital asset management, Internet of Things, inventory management",
author = "Mona Esmaeili and Zeinab Akhavan and Hamid Nasiri and Worku, {Yonatan Melese} and Arzo, {Sisay Tadesse} and Andreas Stavropoulos and Michael Devetsikiotis and Payman Zarkesh-Ha",
year = "2023",
doi = "10.1109/ICMLA58977.2023.00140",
language = "English",
series = "Proceedings - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "944--951",
editor = "{Arif Wani}, M. and Mihai Boicu and Moamar Sayed-Mouchaweh and Abreu, {Pedro Henriques} and Joao Gama",
booktitle = "Proceedings - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023",
note = "22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023 ; Conference date: 15-12-2023 Through 17-12-2023",
}
RIS
TY - GEN
T1 - Medical Asset Management
T2 - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023
AU - Esmaeili, Mona
AU - Akhavan, Zeinab
AU - Nasiri, Hamid
AU - Worku, Yonatan Melese
AU - Arzo, Sisay Tadesse
AU - Stavropoulos, Andreas
AU - Devetsikiotis, Michael
AU - Zarkesh-Ha, Payman
PY - 2023
Y1 - 2023
N2 - Periodic Automatic Replenishment (PAR) is an inventory management policy that assists the healthcare sector in keeping the right amount of stock on hand to avoid excess stock and the potential for products to expire. Traditionally, hospitals rely on the experience and firsthand knowledge of stock management technicians to keep their store supplies with enough equipment. However, manual management based on 'gut feeling' and/or nursing feedback may lead to missing products or incorrect stock orders. Extracting accurate data is often too complex or time-consuming, resulting in a lack of critical reporting data to manage product inventories across the organization properly. However, adopting forecasting techniques and incorporating them into traditional PAR management policies can provide efficient solutions for controlling hospital warehouse inventory at the lowest cost. We have proposed a deep learning-based framework to monitor inventories to reduce costs and variation, create efficiencies, and improve the quality of patient care in hospitals. Furthermore, the proposed forecasting framework's performance is assessed using a real-world scenario within a hospital. The findings indicate that the system is capable of accurately tracking the usage of medical equipment, which results in a significant reduction in the unavailability of assets.
AB - Periodic Automatic Replenishment (PAR) is an inventory management policy that assists the healthcare sector in keeping the right amount of stock on hand to avoid excess stock and the potential for products to expire. Traditionally, hospitals rely on the experience and firsthand knowledge of stock management technicians to keep their store supplies with enough equipment. However, manual management based on 'gut feeling' and/or nursing feedback may lead to missing products or incorrect stock orders. Extracting accurate data is often too complex or time-consuming, resulting in a lack of critical reporting data to manage product inventories across the organization properly. However, adopting forecasting techniques and incorporating them into traditional PAR management policies can provide efficient solutions for controlling hospital warehouse inventory at the lowest cost. We have proposed a deep learning-based framework to monitor inventories to reduce costs and variation, create efficiencies, and improve the quality of patient care in hospitals. Furthermore, the proposed forecasting framework's performance is assessed using a real-world scenario within a hospital. The findings indicate that the system is capable of accurately tracking the usage of medical equipment, which results in a significant reduction in the unavailability of assets.
KW - deep learning
KW - hospital asset management
KW - Internet of Things
KW - inventory management
U2 - 10.1109/ICMLA58977.2023.00140
DO - 10.1109/ICMLA58977.2023.00140
M3 - Conference contribution/Paper
AN - SCOPUS:85190117881
T3 - Proceedings - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023
SP - 944
EP - 951
BT - Proceedings - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023
A2 - Arif Wani, M.
A2 - Boicu, Mihai
A2 - Sayed-Mouchaweh, Moamar
A2 - Abreu, Pedro Henriques
A2 - Gama, Joao
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 15 December 2023 through 17 December 2023
ER -
