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StrokeClassifier:通过使用电子健康记录的整体共识建模对缺血性中风病因的分类.

Ho-Joon Lee1, Lee H Schwamm2,3, Lauren H Sansing3

  • 1Department of Genetics and Yale Center for Genome Analysis, Yale School of Medicine, New Haven, CT, USA. ho-joon.lee@yale.edu.

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概括

一个人工智能工具StrokeClassifier准确地使用电子健康记录预测急性缺血性中风的原因. 这种人工智能系统与神经科医生的表现相美,有助于识别中风病因,以更好地预防.

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科学领域:

  • 神经学 神经学
  • 人工智能的人工智能
  • 医疗信息学 医疗信息学

背景情况:

  • 确定急性缺血性中风 (AIS) 病因对于二次预防至关重要,但在诊断上仍然具有挑战性.
  • 准确的病因分类指导了针对性的治疗策略,并改善了患者的治疗结果.
  • 现有的方法依赖于专家审查,这可能是耗时和资源密集的.

研究的目的:

  • 开发和验证一个自动分类工具StrokeClassifier,用于预测AIS病因.
  • 用电子健康记录 (EHR) 数据对 StrokeClassifier 与专家诊断的性能进行评估.
  • 评估StrokeClassifier在减少密码性中风诊断方面的潜力.

主要方法:

  • 经过训练和验证的StrokeClassifier,一个整体共识元模型,基于来自2039名非密码性AIS患者的EHR文本.
  • 使用自然语言处理 (NLP) 来从排放摘要中提取特征.
  • 在MIMIC-III数据集中的406份排放摘要上对该工具进行了外部验证.

主要成果:

  • 对于多类分类,StrokeClassifier实现了0.74的平均交叉验证精度和0.74的加权F1.
  • 在MIMIC-III的外部验证中,准确度为0.70,加权F1为0.71.
  • 该工具在对密码性中风患者应用时,将密码性诊断从25.2%降至7.2%.

结论:

  • 在分类缺血性中风病因方面,StrokeClassifier的表现与血管神经病学家的表现相当.
  • 人工智能工具显示出作为中风诊断临床决策支持系统的前景.
  • 进一步开发可以提高其在现实世界临床环境中的实用性.