The United States’ blood supply chain is experiencing market decline due to recent innovations in surgical practice, transfusion management, and hospital policy. These innovations strain US blood centers, resulting in cuts to surge capacities, consolidation, and reduced funding for research and outreach programs. In this study, we use data from a regional blood center to explore the application of contemporary machine learning algorithms for modeling donor retention. Such predictive models of donor retention can be used to design more cost effective donor outreach programs. Using data from a large US blood center paired with random forest classifiers, we are able to build a model of donor retention with a Mathews correlation of coefficient of 0.851.

由于近期外科手术、输血管理和医院政策方面的创新，美国的血液供应链正在经历市场下滑。这些创新给美国血液中心带来了压力，导致能力激增、整合以及研究和外展计划资金减少。在这项研究中，我们使用来自区域血液中心的数据来探索当代机器学习算法在供体保留建模中的应用。这种捐助者保留的预测模型可用于设计更具成本效益的捐助者外展计划。使用来自美国大型血液中心的数据与随机森林分类器配对，我们能够建立一个马修斯相关系数为 0.851 的供体保留模型。