用机器学习优化血小板发放，减少浪费
Many happy returns: machine learning to support platelet issuing and waste reduction in hospital blood banks
- 基于1.7万次请求训练模型，预测未使用血小板返回概率
- 模拟显示可降低14%的血小板浪费率
- 适合返回率高、有效期短的医院使用
减少医院血库中血小板浪费的努力主要集中在订货策略上，但优先发放最老单位的做法在部分单位被退回时可能并非最优。本文提出一种新型机器学习引导的发放策略，以提高退回血小板在过期前被重新发放的概率。模型基于17,297次血小板请求训练，在9,353条保留请求上达到AUROC 0.74。在开发模型前，我们构建了包含退回情况的血库运行仿真，评估该模型潜在收益。将训练好的模型应用于仿真，估计可减少14%的浪费。合作医院正考虑采纳此方法，尤其适用于退货率较高且到货后剩余有效期较短的机构。
原文摘要 · Abstract (English)
Efforts to reduce platelet wastage in hospital blood banks have focused on ordering policies, but the predominant practice of issuing the oldest unit first may not be optimal when some units are returned unused. We propose a novel, machine learning (ML)-guided issuing policy to increase the likelihood of returned units being reissued before expiration. Our ML model trained to predict returns on 17,297 requests for platelets gave AUROC 0.74 on 9,353 held-out requests. Prior to ML model development we built a simulation of the blood bank operation that incorporated returns to understand the scale of benefits of such a model. Using our trained model in the simulation gave an estimated reduction in wastage of 14%. Our partner hospital is considering adopting our approach, which would be particularly beneficial for hospitals with higher return rates and where units have a shorter remaining useful life on arrival.
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