机器学习可以预测脑血管在患有下关节出血的患者中
David A Zarrin1, Abhinav Suri1, Karen McCarthy2
1David Geffen School of Medicine at University of California, Los Angeles, USA.
EBioMedicine
|June 20, 2024
概括
机器学习可以准确地预测脑血管需要维拉帕米尔 (CVRV) 在脑下关节出血患者. 这种早期预测有助于优化临床管理和资源配置,以获得更好的患者结果.
科学领域:
- 神经学 神经学
- 密集护理医学 密集护理医学
- 医疗保健中的机器学习
背景情况:
- 大脑血管 (CV) 是次大脑下出血 (SAH) 后的常见和严重并发症,发生在20-40%的病例中.
- 目前的管理包括对SAH患者的长期,资源密集的重症监护室 (ICU) 监测.
- 预测需要维拉帕米尔的CV (CVRV) 对于及时干预和资源优化至关重要.
研究的目的:
- 开发和验证用于早期预测SAH患者CVRV的机器学习模型.
- 在住院期间的不同时间点评估模型的准确性和预测能力.
- 建立一个多中心验证的工具,以改善临床决策.
主要方法:
- 利用了包括UCLA (2013-2022) 和VUMC (2018-2023) 的患者在内的多中心数据集.
- 为每个患者提取了172个独特的ICU变量.
- 训练了一个光梯度增强机 (LightGBM) 模型,使用五倍交叉验证来预测CVRV.
主要成果:
- 轻GBM模型实现了0.88的ROC下面面积 (AUC) 预测CVRV提前一周.
- 该模型在预测不同CVRV时间线方面表现出很高的准确性:AUC分别为0.88,0.83和0.88的"没有CVRV"",三天内CVRV"和"三天后CVRV".
- 多中心验证显示机构之间的表现相似,VUMC预测的平均值在UCLA预测的0.01 AUC点内.
结论:
- 使用机器学习开发了一个准确和早期的CVRV预测器,并在多个中心验证.
- 这种预测工具代表了优化SAH患者临床管理和资源配置的重大进步.
- 这些发现支持将机器学习整合到临界神经疾病中的积极患者护理中.
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