Optimizing Blood Transfusions and Predicting Shortages in Resource-Constrained Areas

TL;DR

This paper introduces heuristic algorithms and machine learning models to optimize blood transfusion management and predict shortages in resource-limited areas, improving allocation efficiency and proactive resource planning.

Contribution

It presents a novel combination of heuristic matching algorithms and machine learning for blood resource optimization and shortage prediction in resource-constrained settings.

Findings

Heuristic matching improved blood request acceptance by up to 47.6%.

Linear Regression slightly outperformed other models in shortage prediction.

The approach is scalable and integrates with NoSQL databases for real-time management.

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.