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预测心脏外科手术中需要输血的需求:机器学习算法与巴西人口确定的风险得分之间的比较

Cristiano Berardo Carneiro da Cunha^1,2,3, Tiago Andrade Lima⁴, Diogo Luiz de Magalhães Ferraz³

¹Department of Cardiovascular Research, Harvard Medical School, Boston, Massachusetts, United States of America.

Brazilian journal of cardiovascular surgery

|March 1, 2024

概括

机器学习算法在预测心脏手术患者的输血方面明显优于传统的评分系统. 这一进步为识别需要输血的患者提供了更高的准确性.

科学领域:

心脏病学心脏病学
医疗信息学医疗信息学
机器学习机器学习

背景情况:

输血在心脏手术中很常见,但存在风险.
准确预测输血需求对于患者安全至关重要.
现有的风险评分显示巴西人口的局限性.

研究的目的:

评估机器学习 (ML) 算法,用于预测心脏外科手术中的输血需求.
将ML模型的性能与已建立的评分系统 (TRACK,TRUST) 进行比较.

主要方法:

495名心脏手术患者 (2019-2021) 的回顾性分析.
开发和对ML模型与TRACK和TRUST评分系统进行比较.
使用曲线下的面积 (AUC) 进行性能评估.

主要成果:

最好的ML模型实现了0.7350.0的AUC.
机器学习算法显著超过了 TRACK (AUC:0.6757) 和 TRUST (AUC:0.6622) 的表现.

结论:

ML算法在预测心脏外科手术中输血需求方面表现出卓越的准确性.
ML提供了一个有前途的工具来加强临床决策和患者护理.
建议进行进一步的研究,以优化临床应用的ML模型.

关键词:

输血输血输血输血输血心脏外科手术心脏外科手术机器学习机器学习风险预测风险预测

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