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PRERISK:一个个性化的,基于人工智能和基于统计的中风复发预测器,用于复发性中风.

Giorgio Colangelo1,2, Marc Ribo1,3, Estefanía Montiel2

  • 1Vall d'Hebron Research Institute, Passeig de la Vall d'Hebron, Barcelona, Spain (G.C., M. Ribo, M.O.-G., M.M., Á.G.-T., M. Requena, J.P., J.J., D.R.-L., N.R.-V., F.R., B.T., C.A.M., M. Rubiera).

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概括

预测中风复发是一个挑战. 新的机器学习工具PRERISK提供了个性化的中风复发风险预测,有可能改善患者的自我护理和结果.

关键词:
人工智能的人工智能是人工智能.提高意识 提高意识机器学习是机器学习.预后 预后 预后复发性 复发性 复发性一次性中风中风中风中风中风幸存者可能是幸存者.

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科学领域:

  • 神经学 神经学
  • 数据科学数据科学数据科学
  • 公共卫生 公共卫生

背景情况:

  • 个性化预测中风复发是困难的,但对于患者的自我护理至关重要.
  • 为了应对这一挑战,开发了一种新的统计和机器学习分类器PRERISK.

研究的目的:

  • 开发和验证一种机器学习模型,用于预测随时间推移的个人中风复发风险.
  • 将机器学习模型的性能与传统的考克斯回归进行比较.

主要方法:

  • 在西班牙加泰罗尼亚 (2014-2020年) 诊断患有中风的41,975名患者的临床和社会经济数据的分析.
  • 开发监督机器学习模型,以预测早期,晚期和长期中风复发.
  • 使用C统计和接收器运行特征曲线 (AUC) 下的面积来评估模型准确性.

主要成果:

  • 16.21%的患者在2.69年的中位数随访期间经历了中风复发.
  • 确定的主要预测因素包括从上次中风开始的时间,巴特尔指数,心房动,失脂症,年龄,糖尿病和性别.
  • 机器学习模型在预测90天,91-365天和>365天的复发时比考克斯回归有统计学显著的改善 (AUC分别为0.76,0.60,0.71).

结论:

  • PRERISK提供了一种新的,个性化的,准确的方法来预测中风复发风险.
  • 该模型能够纳入动态风险因素控制,这有助于改善患者管理.