ArticleIndian journal of hematology & blood transfusion : an official journal of Indian Society of Hematology and Blood Transfusion2021
RBC Inventory-Management System Based on XGBoost Model.
Article in Indian journal of hematology & blood transfusion : an official journal of Indian Society of Hematology and Blood Transfusion, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Development of an Explainable Machine Learning Model to Predict Mortality Risk in Sepsis Patients: Insights From a Real-World Clinical Data.Shock (Augusta, Ga.) · 2026Article
- Artificial Intelligence in Patient Blood Management: A Systematic Review of Predictive, Diagnostic, and Decision Support Applications.Journal of clinical medicine · 2025Review
- Optimizing the Hospital Blood Bank Stock in Korea: A Comparative Analysis of the Uniform 5-Day Stock Index and a Novel Blood Stock Index.Annals of laboratory medicine · 2024Article
- Machine learning in transfusion medicine: A scoping review.Transfusion · 2024Article
- Forecasting demand for blood products: Towards inventory management of a perishable product.Bioinformation · 2024Article
- Machine learning-based models for predicting mortality and acute kidney injury in critical pulmonary embolism.BMC cardiovascular disorders · 2023Article
- Blood Inventory Management During COVID-19 Pandemic Using a Simple Mathematical Tool: A Two-Year Study from a Tertiary Care Hospital in North India.Indian journal of hematology & blood transfusion : an official journal of Indian Society of Hematology and Blood Transfusion · 2023Article
- Prediction of Red Blood Cell Demand for Pediatric Patients Using a Time-Series Model: A Single-Center Study in China.Frontiers in medicine · 2022Article
- Machine learning models including insulin resistance indexes for predicting liver stiffness in United States population: Data from NHANES.Frontiers in public health · 2022Article
- Ability of a Machine Learning Algorithm to Predict the Need for Perioperative Red Blood Cells Transfusion in Pelvic Fracture Patients: A Multicenter Cohort Study in China.Frontiers in medicine · 2021Article
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7 authors.
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Abstract
It is difficult to predict RBC consumption accurately. This paper aims to use big data to establish a XGBoost Model to understand the trend of RBC accurately, and forecast the demand in time. XGBoost, which implements machine learning algorithms under the Gradient Boosting framework can provide a parallel tree boosting. The daily RBC usage and inventory (May 2014-September 2017) were investigated, and rules for RBC usage were analysed. All data were divided into training sets and testing sets. A XGBoost Model was established to predict the future RBC demand for durations ranging from a day to a week. In addition, the alert range was added to the predicted value to ensure RBC demand of emergency patients and surgical accidents. The gap between RBC usage and inventory was fluctuant, and had no obvious rule. The maximum residual inventory of a certain blood group was up to 700 units one day, while the minimum was nearly 0 units. Upon comparing MAE (mean absolute error):A:10.69, B:11.19, O:10.93, and AB:5.91, respectively, the XGBoost Model was found to have a predictive advantage over other state-of-the-art approaches. It showed the model could fit the trend of daily RBC usage. An alert range could manage the demand of emergency patients or surgical accidents. The model had been built to predict RBC demand, and the alert range of RBC inventory is designed to increase the safety of inventory management.
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