WALID Ben elmir

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WALID Ben elmir

بن المير وليد

walid.benelmir@univ-msila.dz

05551023XX

  • Informatics Department
  • Faculty of Mathematics and Informatics
  • Grade PHd
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About Me

Domain

Mathématiques et Informatique

Research Domains

machine learning time series forecasting blood supply management

Filiere

Informatique

Location

Alger, Alger

Alger, ALGERIA

  • 1992-09-09 00:00:00

  • WALID Ben elmir birthday

  • 2023
  • 2023

    Smart Platform for Data Blood Bank Management: Forecasting Demand in Blood Supply Chain Using Machine Learning

    Despite the efforts of the World Health Organization, blood transfusions and delivery are still the crucial challenges in blood supply chain management, especially when there is a high demand and not enough blood inventory. Consequently, reducing uncertainty in blood demand, waste, and shortages has become a primary goal. In this paper, we propose a smart platform-oriented approach that will create a robust blood demand and supply chain able to achieve the goals of reducing uncertainty in blood demand by forecasting blood collection/demand, and reducing blood wastage and shortage by balancing blood collection and distribution based on an effective blood inventory management. We use machine learning and time series forecasting models to develop an AI/ML decision support system. It is an effective tool with three main modules that directly and indirectly impact all phases of the blood supply chain: (i) the blood demand forecasting module is designed to forecast blood demand; (ii) blood donor classification helps predict daily unbooked donors thereby enhancing the ability to control the volume of blood collected based on the results of blood demand forecasting; and (iii) scheduling blood donation appointments according to the expected number and type of blood donations, thus improving the quantity of blood by reducing the number of canceled appointments, and indirectly improving the quality and quantity of blood supply by decreasing the number of unqualified donors, thereby reducing the amount of invalid blood after and before preparation. As a result of the system’s improvements, blood shortages and waste can be reduced. The proposed solution provides robust and accurate predictions and identifies important clinical predictors for blood demand forecasting. Compared with the past year’s historical data, our integrated proposed system increased collected blood volume by 11%, decreased inventory wastage by 20%, and had a low incidence of shortages.

    Citation

    WALID BEN ELMIR , ALLAOUA Hemmak , Benaoumeur Senouci, , (2023), Smart Platform for Data Blood Bank Management: Forecasting Demand in Blood Supply Chain Using Machine Learning, Information, Vol:14, Issue:1, pages:31, MDPI

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  • 2023
  • 2023

    Smart platform for Blood Management in Healthcare using AI/ML Approach

    The blood management system confronts a challenge with blood transfusions and their distribution regardless of the efforts of the World Health Organization and other global health organizations: inadequate supply, excessive demand, and a shortage of accessible blood. Due to its ability to raise labor efficiency and service quality via systematic management, artificial intelligence is currently necessary to enhance blood supply operations. The objective of this work is to provide an AI/ML platform that facilitates the use of data to assist health professionals in making the most effective management choices that are consistent with methods for minimizing waste and costs. By more accurately anticipating blood demand. As production models, we are using both time series and machine learning methods as prediction models. The optimal performance model for the provided case study was determined by comparing the performance outcomes of each method. In this work, autoregressive Moving Average models, autoregressive Integrated Moving Average models, and seasonal ARIMA models are applied. In addition, we used four native algorithms for machine learning: Artificial Neural Networks, Linear Regression, and Support Vector Regression. The results demonstrate that both types of forecasting models can significantly enhance the management of the blood supply.

    Citation

    WALID BEN ELMIR , ALLAOUA Hemmak , Benaoumeur Senouci, ,(2023), Smart platform for Blood Management in Healthcare using AI/ML Approach,International Conference on Artificial Intelligence in Information and Communication (ICAIIC),Bali, Indonesia

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  • 2022
  • 2022

    Data Blood Management for Algerian Healthcare System : Smart Platform Oriented Approach

    Information and computer technology are gaining popularity in blood banks because of their potential to enhance labor productivity and service quality. This article focuses on the significance of web-based technologies in blood bank information management in order to improve the efficacy of this vital, particularly challenging activity in developing nations. Despite the efforts of the World Health Organization and others, blood transfusions and their delivery pose a problem in developing countries: insufficient supply, high demand, and inadequate availability. The research revealed that blood banking institutions in developing countries lacked coordination, as each blood bank maintained its own records that were not shared with other banks. The objective of this paper is to propose a solution that: (1) facilitates coordination between blood banks, health institutions, and donors by interacting with a central database. (2) Providing a solution to one of the most important issues of blood bank information systems, namely, how to effectively use the growing amount of data and information to aid in decisionmaking. (3) Proposes a decision support system for blood supply management that enables the incorporation of human knowledge into an automated system to improve the efficacy of blood bank management; the objective is to assist health administrators in making the best management decisions that are in line with the best strategies.

    Citation

    WALID BEN ELMIR , ALLAOUA Hemmak , Benaoumeur Senouci, ,(2022), Data Blood Management for Algerian Healthcare System : Smart Platform Oriented Approach,5th International Conference on Embedded Systems in Telecommunications and Instrumentation,Annaba, Algeria

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