在接受关节整形手术的老年患者中预测红细胞输血的可解释的人工智能:使用国家医疗保险数据进行机器学习分析
Hyunyoung Seong1, Kwang-Sig Lee2, Yumin Choi3
1Department of Anesthesiology and Pain Medicine, Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea.
Medicine
|February 23, 2024
概括
贫血,胺酸,年龄和麻醉是关节关节整形手术患者输血的关键预测因素. 机器学习有效地识别了针对性干预的高风险个体,改善了患者的治疗结果.
科学领域:
- 整形外科手术 整形外科手术
- 数据科学数据科学数据科学
- 医疗保健服务研究 医疗服务研究
背景情况:
- 输血是关节整形手术的重要问题.
- 识别输血的预测因素可以优化患者管理和资源分配.
研究的目的:
- 用机器学习分析接受关节整形手术的老年患者输血的主要决定因素.
- 确定针对性干预的高风险患者群体.
主要方法:
- 来自韩国国家医疗保险服务索赔数据 (2019年) 的19,110名65岁以上患者的回顾性队列研究.
- 机器学习模型 (随机森林,沙普利附加解释) 用于识别和排名输血的预测因素.
- 用曲线下的面积 (AUC) 来评估模型性能.
主要成果:
- 随机森林模型实现了73.6%的AUC.
- 输血的顶级预测因素包括贫血,松酸,年龄,社会经济地位,脊髓麻醉,全身麻醉,性别 (女性),痴呆症,铁和充血性心力衰竭.
- 贫血,年龄,松酸和脊髓麻醉与输血概率的增加有关.
结论:
- 机器学习为关节整形手术患者的输血提供了一个有效的预测模型.
- 患有贫血,晚年和并发症的患者是高风险群体,需要进行干预,如胺酸和铁补充剂.
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