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使用机器学习来预测心脏手术后的出血

Victor Hui^1,2, Edward Litton^3,4, Cyrus Edibam³

¹Department of Anaesthesia and Pain Medicine, Royal Melbourne Hospital, Melbourne, VIC, Australia.

European journal of cardio-thoracic surgery : official journal of the European Association for Cardio-thoracic Surgery

|September 5, 2023

概括

机器学习模型使用各种患者数据准确地预测心脏手术后的出血. 这种方法增强了对外科手术期间出血事件的预测,改善了患者的治疗结果.

科学领域:

医疗信息学医疗信息学
心血管外科心血管外科
医疗保健中的机器学习

背景情况:

心脏手术后出血是严重的并发症.
准确预测出血对于患者管理至关重要.
现有的预测方法可能无法充分利用全面的患者数据.

研究的目的:

开发和评估用于预测心脏手术后外科手术期间出血的机器学习模型.
整合来自多个来源的数据,包括手术,输液,ICU和实验室记录.
为了比较不同机器学习算法在出血预测中的性能.

主要方法:

利用了2000名心脏手术患者 (2015年2月 - 2022年3月) 的数据.
训练机器学习模型来预测使用Papworth和Dyke等的出血. 定义. 定义. 这些定义.
使用AUROC和AUPRC等指标评估模型性能.

主要成果:

集体投票分类器表现出了最佳表现.
在帕普沃思定义中,AUPRC达到0.310 (AUROC为0.738).
达到了0.452的AUPRC (AUROC 0.797) 对于出血的戴克定义.

结论:

机器学习有效地预测心脏手术后的出血.
从各种来源例行收集的数据可以整合到预测中.
这种预测能力可以帮助临床决策和患者护理.

关键词:

流血出血流血出血心脏外科手术的心脏手术机器学习是机器学习.

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