RBC Inventory-Management System Based on XGBoost Model
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ORIGINAL ARTICLE
RBC Inventory-Management System Based on XGBoost Model Xiaolin Sun1 • Zhenhua Xu2 • Yannan Feng1 • Qingqing Yang2 • Yan Xie2 Deqing Wang1 • Yang Yu1
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Received: 12 January 2020 / Accepted: 6 August 2020 Ó Indian Society of Hematology and Blood Transfusion 2020
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.
Xiaolin Sun and Zhenhua Xu have contributed equally to this article.
Electronic supplementary material The online version of this article (https://doi.org/10.1007/s12288-020-01333-5) contains supplementary material, which is available to authorized users.
Keywords XGBoost model RBC inventory Big data Transfusion prediction
& Deqing Wang [email protected]
Introduction
& Yang Yu [email protected]
Blood transfusion has become a common clinical strategy. Although surgical techniques and clinical treatment methods are being constantly updated, they are still impossible to avoid massive blood loss during the perioperative period or blood transfusion support during the treatment of internal medical diseases. RBC, which is the most important blood component, can be targeted for treating anaemia. However, blood resources may face the shortage, or imbalance between blood collection and usage. In recent years, with the rapid development of the medical industry in China, the imbalance between RBC supply and demand is becoming increasingly prominent. The challenge in RBC inventory management is to find a balance point between the two. Thus far, hospitals in many
Xiaolin Sun [email protected] Zhenhua Xu [email protected] Qingqing Yang [email protected] Yan Xie [email protected] 1
Department of Blood Transfusion,
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