Predicting Blood Donors Using Machine Learning Techniques

被引：10

作者：

Kauten, Christian ^{[1
]}

Gupta, Ashish ^{[2
]}

Qin, Xiao ^{[1
]}

Richey, Glenn ^{[3
]}

机构：

[1] Auburn Univ, Samuel Ginn Coll Engn, Comp Sci & Software Engn, Auburn, AL 36849 USA

[2] Auburn Univ, Harbert Coll Business, Dept Syst & Technol, Auburn, AL 36849 USA

[3] Auburn Univ, Harbert Coll Business, Dept Supply Chain Management, Auburn, AL 36849 USA

来源：

INFORMATION SYSTEMS FRONTIERS | 2022年 / 24卷 / 05期

基金：

美国国家科学基金会;

关键词：

Analytics; Blood donors; Blood supply; Machine learning; Retention; RETENTION; CELLS;

D O I：

10.1007/s10796-021-10149-1

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：1547 / 1562

页数：16

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