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利用基于国家公共大数据的人工智能开发血液需求预测模型

Hi Jeong Kwon¹, Sholhui Park², Young Hoon Park³

¹Department of Laboratory Medicine, Yeouido St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea.

|January 22, 2024

概括

这项研究开发了一种使用国家大数据的AI模型,用于预测每月输血需求,改善血液供应稳定性. 该算法有效地预测需求,确保医疗设施有足够的血液制品.

科学领域:

医疗保健分析医疗保健分析
生物医学信息学生物医学信息学
数据科学在医学中的数据科学

背景情况:

医疗保健系统在维持稳定和充足的血液供应方面面临着持续的挑战.
血液短缺可能会严重影响患者的护理和治疗结果.
准确预测血液需求对于有效的库存管理至关重要.

研究的目的:

开发一种人工智能 (AI) 模型,用于预测每月输血需求.
利用与输血相关的国家开放大数据进行准确的需求预测.
提高医疗机构血液供应的稳定性和充足性.

主要方法:

利用韩国国家对血液类型和成分 (2010-2021) 的开放大数据.
开发了预测模型,包括极端梯度提升 (XGBoost),光梯度提升机 (LGBM) 和CatBoost.
创建了一个组合模型,将三个单独的预测模型结合起来.

主要成果:

个别模型 (XGBoost,LGBM,CatBoost) 显示不同血液产品和类型的预测准确度不同.
合并模型显著提高了预测性能,减少了平均绝对值和根平均平方误差.
请求部门是最有影响力的因素;其他关键特征包括RBC抗体查,交叉匹配数据,捐赠和手术.

结论:

开发的AI驱动的血液需求预测算法有效预测输血需求.
该工具使医疗机构能够主动管理血液库存,并确保充足的供应.
利用开放的大数据提高了医疗保健中血液资源分配的效率.

关键词:

输血输血输血是指输血.人工智能的人工智能是人工智能.大数据就是大数据.提升模型的提升模型预测模型预测模型

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