Optimizing Blood Transfusions and Predicting Shortages in Resource-Constrained Areas

El Arbi Belfarsi, Sophie Brubaker, Maria Valero

2025

Abstract

Our research addresses the critical challenge of managing blood transfusions and optimizing allocation in resource-constrained regions. We present heuristic matching algorithms for donor-patient and blood bank selection, alongside machine learning methods to analyze blood transfusion acceptance data and predict potential shortages. We developed simulations to optimize blood bank operations, progressing from random allocation to a system incorporating proximity-based selection, blood type compatibility, expiration prioritization, and rarity scores. Moving from blind matching to a heuristic-based approach yielded a 28.6% marginal improvement in blood request acceptance, while a multi-level heuristic matching resulted in a 47.6% improvement. For shortage prediction, we compared Long Short-Term Memory (LSTM) networks, Linear Regression, and AutoRegressive Integrated Moving Average (ARIMA) models, trained on 170 days of historical data. Linear Regression slightly outperformed others with a 1.40% average absolute percentage difference in predictions. Our solution leverages a Cassandra NoSQL database, integrating heuristic optimization and shortage prediction to proactively manage blood resources. This scalable approach, designed for resource-constrained environments, considers factors such as proximity, blood type compatibility, inventory expiration, and rarity. Future developments will incorporate real-world data and additional variables to improve prediction accuracy and optimization performance.

Paper Citation

in Harvard Style

Belfarsi E., Brubaker S. and Valero M. (2025). Optimizing Blood Transfusions and Predicting Shortages in Resource-Constrained Areas. In Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF; ISBN 978-989-758-731-3, SciTePress, pages 149-160. DOI: 10.5220/0013182700003911

in Bibtex Style

@conference{healthinf25,
author={El Belfarsi and Sophie Brubaker and Maria Valero},
title={Optimizing Blood Transfusions and Predicting Shortages in Resource-Constrained Areas},
booktitle={Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF},
year={2025},
pages={149-160},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013182700003911},
isbn={978-989-758-731-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF
TI - Optimizing Blood Transfusions and Predicting Shortages in Resource-Constrained Areas
SN - 978-989-758-731-3
AU - Belfarsi E.
AU - Brubaker S.
AU - Valero M.
PY - 2025
SP - 149
EP - 160
DO - 10.5220/0013182700003911
PB - SciTePress