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Artificial Neural Network Based Approach for Blood Demand Forecasting: Fez Transfusion Blood Center Case Study

Authors:

Rohaifa Khaldi,

Abdellatif El Afia,

Raddouane Chiheb,

Rdouan FaiziAuthors Info & Claims

BDCA'17: Proceedings of the 2nd international Conference on Big Data, Cloud and Applications

Article No.: 59, Pages 1 - 6

https://doi.org/10.1145/3090354.3090415

Published: 29 March 2017 Publication History

Abstract

Blood demand and supply management are considered one of the major components of a healthcare supply chain, since blood is a vital element in preserving patient's life. However, forecasting it faces several challenges including frequent shortages, and possible expiration caused by demand uncertainty of hospitals. This uncertainty is mainly due to high variability in the number of emergency cases. Thereupon, this investigation presents a real case study of forecasting monthly demand of three blood components, using Artificial Neural Networks (ANNs). The demand of the three blood components (red blood cells (RBC), plasma (CP) and platelets (PFC)) and other observations are obtained from a central transfusion blood center and a University Hospital. Experiments are carried out using three networks to forecast each blood component separately. Last, the presented model is compared with ARIMA to evaluate its performance in prediction. The results of this study depict that ANN models overcomes ARIMA models in demand forecasting. Thus high ANN models can be considered as a promising approach in forecasting monthly blood demand.

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Cited By

Amel YSelma LMenaouer B(2025)Blood Product Prediction Using Supervised Machine LearningIoT-Enabled Energy Efficiency Assessment of Renewable Energy Systems and Micro-grids in Smart Cities10.1007/978-3-031-60632-8_30(350-362)Online publication date: 3-Jan-2025
https://doi.org/10.1007/978-3-031-60632-8_30
Motamedi MDawson JLi NDown DHeddle N(2024)Demand forecasting for platelet usage: From univariate time series to multivariable modelsPLOS ONE10.1371/journal.pone.029739119:4(e0297391)Online publication date: 23-Apr-2024
https://doi.org/10.1371/journal.pone.0297391
Rastgou MHe YJiang Q(2024)Implementation and efficient evaluation of backpropagation network training algorithms in parametric simulations of soil hydraulic conductivity curveJournal of Hydrology10.1016/j.jhydrol.2024.131302636(131302)Online publication date: Jun-2024
https://doi.org/10.1016/j.jhydrol.2024.131302
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Index Terms

Artificial Neural Network Based Approach for Blood Demand Forecasting: Fez Transfusion Blood Center Case Study
1. Applied computing
  1. Operations research
    1. Forecasting
2. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks

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Published In

cover image ACM Other conferences

BDCA'17: Proceedings of the 2nd international Conference on Big Data, Cloud and Applications

March 2017

685 pages

ISBN:9781450348522

DOI:10.1145/3090354

Conference Chairs:
Mohamed Lazaar
ENSA, Tetuan, Morocco
,
Youness Tabii
ENSA, Tetuan, Morocco
,
Mohamed Chrayah
ENSA, Tetuan - Morocco
,
Mohammed Al Achhab
ENSA, Tetuan, Morocco

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Ministère de I'enseignement supérieur: Ministère de I'enseignement supérieur

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 March 2017

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BDCA'17

BDCA'17: 2nd international Conference on Big Data, Cloud and Applications

March 29 - 30, 2017

Tetouan, Morocco

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Cited By

Amel YSelma LMenaouer B(2025)Blood Product Prediction Using Supervised Machine LearningIoT-Enabled Energy Efficiency Assessment of Renewable Energy Systems and Micro-grids in Smart Cities10.1007/978-3-031-60632-8_30(350-362)Online publication date: 3-Jan-2025
https://doi.org/10.1007/978-3-031-60632-8_30
Motamedi MDawson JLi NDown DHeddle N(2024)Demand forecasting for platelet usage: From univariate time series to multivariable modelsPLOS ONE10.1371/journal.pone.029739119:4(e0297391)Online publication date: 23-Apr-2024
https://doi.org/10.1371/journal.pone.0297391
Rastgou MHe YJiang Q(2024)Implementation and efficient evaluation of backpropagation network training algorithms in parametric simulations of soil hydraulic conductivity curveJournal of Hydrology10.1016/j.jhydrol.2024.131302636(131302)Online publication date: Jun-2024
https://doi.org/10.1016/j.jhydrol.2024.131302
Dauphinet MHenriques RMonteiro P(2024)Context-Aware Deep Forecasting: Principles for the Nation-Wide Management of Blood ProductsProgress in Artificial Intelligence10.1007/978-3-031-73503-5_29(359-372)Online publication date: 3-Sep-2024
https://dl.acm.org/doi/10.1007/978-3-031-73503-5_29
Rezki NMansouri M(2024)Artificial Neural Network for Enhancing Supply Chain Risk ManagementDigital Technologies and Applications10.1007/978-3-031-68653-5_41(433-442)Online publication date: 3-Sep-2024
https://doi.org/10.1007/978-3-031-68653-5_41
Ben Elmir WHemmak ASenouci B(2023)Smart Platform for Data Blood Bank Management: Forecasting Demand in Blood Supply Chain Using Machine LearningInformation10.3390/info1401003114:1(31)Online publication date: 5-Jan-2023
https://doi.org/10.3390/info14010031
Ahmadimanesh MSafabakhsh HSadeghi S(2023)Designing an optimal model of blood logistics management with the possibility of return in the three-level blood transfusion networkBMC Health Services Research10.1186/s12913-023-10240-023:1Online publication date: 27-Nov-2023
https://doi.org/10.1186/s12913-023-10240-0
Pan LZang ZMa SHu WHu Z(2023)BUPNNComputational Intelligence and Neuroscience10.1155/2023/10033102023Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1155/2023/1003310
El-rashidy NEl-Ghamry AElSayed N(2023)Machine Learning for Blood Donors Classification Model Using Ensemble LearningGreen Sustainability: Towards Innovative Digital Transformation10.1007/978-981-99-4764-5_11(173-181)Online publication date: 16-Nov-2023
https://doi.org/10.1007/978-981-99-4764-5_11
Kasten J(2022)Big Data Applications in Healthcare AdministrationResearch Anthology on Big Data Analytics, Architectures, and Applications10.4018/978-1-6684-3662-2.ch048(1003-1034)Online publication date: 2022
https://doi.org/10.4018/978-1-6684-3662-2.ch048
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