Predicting willingness to donate blood based on machine learning: two blood donor recruitments during COVID-19 outbreaks

Abstract

Machine learning methods are a novel way to predict and rank donors' willingness to donate blood and to achieve precision recruitment, which can improve the recruitment efficiency and meet the challenge of blood shortage. We collected information about experienced blood donors via short message service (SMS) recruitment and developed 7 machine learning-based recruitment models using PyCharm-Python Environment and 13 features which were described as a method for ranking and predicting donors’ intentions to donate blood with a floating number between 0 and 1. Performance of the prediction models was assessed by the Area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, and F1 score in the full dataset, and by the accuracy in the four sub-datasets. The developed models were applied to prospective validations of recruiting experienced blood donors during two COVID-19 pandemics, while the routine method was used as a control. Overall, a total of 95,476 recruitments via SMS and their donation results were enrolled in our modelling study. The strongest predictor features for the donation of experienced donors were blood donation interval, age, and donation frequency. Among the seven baseline models, the eXtreme Gradient Boosting (XGBoost) and Support vector machine models (SVM) achieved the best performance: mean (95%CI) with the highest AUC: 0.809 (0.806–0.811), accuracy: 0.815 (0.812–0.818), precision: 0.840 (0.835–0.845), and F1 score of XGBoost: 0.843 (0.840–0.845) and recall of SVM: 0.991 (0.988–0.994). The hit rate of the XGBoost model alone and the combined XGBoost and SVM models were 1.25 and 1.80 times higher than that of the conventional method as a control in 2 recruitments respectively, and the hit rate of the high willingness to donate group was 1.96 times higher than that of the low willingness to donate group. Our results suggested that the machine learning models could predict and determine the experienced donors with a strong willingness to donate blood by a ranking score based on personalized donation data and demographical details, significantly improve the recruitment rate of blood donors and help blood agencies to maintain the blood supply in emergencies.

A systematic review of predictive blood donor retention models.

“…Using the dataset from Yangzhou Blood Station in China, Wu et al 2022 gathered information about experienced blood donors recruited via short message service (SMS) and developed seven machine learning-based recruitment models [7]. Thirteen characteristics were outlined as a method for evaluating and predicting blood donors' intentions to donate.…”

Harnessing the potential of data‐driven strategies to optimise transfusion practice

“…78,79 There are increasing number of publications reporting advanced techniques to predict transfusion needs in patients. [80][81][82][83][84] Recent studies have shown the potential for data on transfusion usage to forecast the demand for both red blood cell [85][86][87] and platelet transfusion requirement. [88][89][90][91] How widely and rapidly these findings can be rolled out in hospital information systems remains to be seen.…”

“…Directly embedding learning systems within EHRs has huge potential but their implementation remains a significant challenge 78,79 . There are increasing number of publications reporting advanced techniques to predict transfusion needs in patients 80–84 . Recent studies have shown the potential for data on transfusion usage to forecast the demand for both red blood cell 85–87 and platelet transfusion requirement 88–91 .…”

Association Between Concerns About COVID-19 Infection and Blood Donation Intention: Cross-Sectional Survey Study Through a Mobile Communication Platform

“…They found that self-efficacy and approval from others, underpinned by coping appraisals and organizational trust, play a critical role in the intention to donate [20]. A study in Jiangsu, China, used machine learning methods to predict blood donation intentions and to improve the recruitment rate of blood donors [21].…”