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Related Experiment Video

Updated: Nov 14, 2025

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
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RBC Inventory-Management System Based on XGBoost Model.

Xiaolin Sun1, Zhenhua Xu2, Yannan Feng1

  • 1Department of Blood Transfusion, Chinese PLA General Hospital, No. 28, Fuxing Rd, Beijing, 100853 China.

Indian Journal of Hematology & Blood Transfusion : an Official Journal of Indian Society of Hematology and Blood Transfusion
|March 12, 2021
PubMed
Summary
This summary is machine-generated.

Predicting red blood cell (RBC) consumption is challenging. This study uses big data and an XGBoost model to accurately forecast RBC demand, improving inventory management and ensuring supply for emergencies.

Keywords:
Big dataRBC inventoryTransfusion predictionXGBoost model

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Area of Science:

  • Biomedical Engineering
  • Data Science
  • Healthcare Management

Background:

  • Accurate prediction of red blood cell (RBC) consumption is critical for effective inventory management.
  • Fluctuations in RBC usage and inventory levels present significant challenges for healthcare providers.
  • Existing methods struggle to accurately forecast RBC demand, leading to potential shortages or wastage.

Purpose of the Study:

  • To develop and validate an XGBoost model for accurate prediction of daily RBC demand.
  • To forecast RBC demand over short-term durations (1 day to 1 week).
  • To implement an alert range system for proactive inventory management, especially for emergency situations.

Main Methods:

  • Utilized big data analytics on daily RBC usage and inventory data from May 2014 to September 2017.
  • Applied machine learning algorithms within the Gradient Boosting framework to build an XGBoost model.
  • Divided data into training and testing sets for model development and validation.
  • Incorporated an alert range based on predicted demand to enhance safety stock levels.

Main Results:

  • The XGBoost model demonstrated a strong ability to fit the trend of daily RBC usage.
  • Compared to other state-of-the-art approaches, the XGBoost model showed a predictive advantage, with low mean absolute errors (MAE) across blood groups (e.g., AB: 5.91).
  • The model successfully predicted future RBC demand, with an added alert range for managing unexpected needs.

Conclusions:

  • The developed XGBoost model provides an accurate and reliable method for forecasting RBC demand.
  • The integration of an alert range enhances RBC inventory safety and management efficiency.
  • This approach can significantly improve blood supply chain logistics and patient care by ensuring timely availability of RBCs.