有(最佳)牛奶吗?利用机器学习和优化技术在母乳库中汇集捐赠

Timothy C. Y. Chan, Rafid Mahmood, Deborah L. O’Connor, Debbie Stone, Sharon Unger, Rachel K. Wong, Ian Yihang Zhu
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引用次数: 0

摘要

问题定义:人类捐献的母乳每年为数百万早产婴儿提供重要的营养。捐赠的母乳由母乳银行收集、加工和分发。供体奶的常量营养素含量与婴儿大脑发育直接相关,并且在不同的供体奶中可能会有很大的差异,这就是为什么多次供体奶通常被集中在一起以产生最终产品的原因。北美大约一半的母乳银行没有资源来测量捐赠母乳的大量营养素含量,这意味着汇集是启发式的。对于这些母乳银行,需要一种方法来优化汇集决策。方法/结果:我们提出了一个数据驱动的框架,结合机器学习和优化来预测捐赠的宏量营养素含量,然后将它们分别优化组合在池中。通过与合作伙伴母乳银行的合作,我们收集了一组牛奶数据来训练我们的预测模型。我们严格模拟母乳银行的实践,以微调我们的优化模型,并评估COVID-19大流行期间捐赠习惯变化等运营情景。最后,我们进行了为期一年的试验实施,在我们的干预之后,我们观察了目前由护士主导的汇集实践。通过我们的方法创建的配方池达到临床宏量营养素目标的几率比基线高出约31%,尽管配方创建时间减少了60%。管理意义:这是结合机器学习和优化的广泛混合文献中的第一篇论文。我们证明了这种管道在医疗保健环境中是可行的,并且可以比当前的实践产生显著的改进。我们的见解可以指导任何应用领域的从业者寻求实现机器学习和基于优化的决策支持。历史:本文已被接受为2022年制造业&服务营运管理实务研究比赛。补充材料:电子伴侣可在https://doi.org/10.1287/msom.2022.0455上获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Got (Optimal) Milk? Pooling Donations in Human Milk Banks with Machine Learning and Optimization
Problem definition: Human donor milk provides critical nutrition for millions of infants who are born preterm each year. Donor milk is collected, processed, and distributed by milk banks. The macronutrient content of donor milk is directly linked to infant brain development and can vary substantially across donations, which is why multiple donations are typically pooled together to create a final product. Approximately half of all milk banks in North America do not have the resources to measure the macronutrient content of donor milk, which means pooling is done heuristically. For these milk banks, an approach is needed to optimize pooling decisions. Methodology/results: We propose a data-driven framework combining machine learning and optimization to predict macronutrient content of donations and then optimally combine them in pools, respectively. In collaboration with our partner milk bank, we collect a data set of milk to train our predictive models. We rigorously simulate milk bank practices to fine-tune our optimization models and evaluate operational scenarios such as changes in donation habits during the COVID-19 pandemic. Finally, we conduct a year-long trial implementation, where we observe the current nurse-led pooling practices followed by our intervention. Pools created by our approach meet clinical macronutrient targets approximately 31% more often than the baseline, although taking 60% less recipe creation time. Managerial implications: This is the first paper in the broader blending literature that combines machine learning and optimization. We demonstrate that such pipelines are feasible to implement in a healthcare setting and can yield significant improvements over current practices. Our insights can guide practitioners in any application area seeking to implement machine learning and optimization-based decision support.History: This paper has been accepted as part of the 2022 Manufacturing & Service Operations Management Practice-Based Research Competition.Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2022.0455 .
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