Establishment of amachine learning-based precision recruitment method at the county level
10.13303/j.cjbt.issn.1004-549x.2025.12.016
- VernacularTitle:县域级基于机器学习的精准招募方法的建立
- Author:
Xiaoyan FU
1
;
Zihan ZHANG
2
;
Fang ZHAO
3
;
Chunlan ZHOU
1
;
Wenbiao LIANG
3
;
Cheng YU
4
;
Yingzhi YAN
4
;
Wei SI
5
;
Weibin TAN
1
;
Hui XUE
6
Author Information
1. Taicang Blood Center, Taicang 215400, China
2. College of Software Engineering, Southeast University, Nanjing 210096, China; Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, Nanjing 210096, China
3. Jiangsu Blood Center, Nanjing 210042, China
4. Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, Nanjing 210096, China; Taicang Blood Center, Taicang 215400, China
5. Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, Nanjing 210096, China; Ht Nanjing School, Nanjing 211113, China
6. Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, Nanjing 210096, China; School of Computer Science and Engineering, Southeast University, Nanjing 210096, China
- Publication Type:Journal Article
- Keywords:
blood donor recruitment;
SMS recruitment;
precision recruitment;
machine learning;
artificial Intelligence
- From:
Chinese Journal of Blood Transfusion
2025;38(12):1752-1758
- CountryChina
- Language:Chinese
-
Abstract:
Objective: To establish a machine learning-based precision blood donor recruitment model at the county level and assess its generalizability and applicability. Methods: A retrospective study was conducted using blood donation and SMS recruitment data from the Taicang Branch of the Suzhou Blood Center between 2019 and 2024. Multiple machine learning algorithms were employed, including extreme gradient boosting, support vector machine, k-nearest neighbor, logistic regression, decision tree, random forest, and multilayer perceptron. These were combined with techniques such as synthetic minority oversampling, undersampling, and cost-sensitive learning (using MFE and MSFE loss functions). Model parameters were optimized through grid search to identify the best-performing model. Results: In a prospective comparative study against conventional methods, the machine learning models increased the recruitment success rate among high-willingness donors by an average of 129.15%, and the recruitment efficiency per SMS improved by 125.02% compared with the traditional method. Under full-scale SMS sending, the recruitment rate per SMS increased by 42.61%, and SMS sending efficiency improved by 31.77%, significantly enhancing recruitment performance. Conclusion: This study represents the first application of a machine learning-based precision donor recruitment model at the county-level in China. The precise recruitment framework not only improves recruitment efficiency and reduces recruitment costs but also demonstrates strong scalability and generalizability. It provides a scientific and feasible intelligent pathway to ensure the safety and sustainability of the blood supply.
