人工智能和机器学习在意识障碍中的作用
Minji Lee1, Steven Laureys2,3,4,5
1Department of Biomedical Software Engineering, The Catholic University of Korea, Bucheon, Republic of Korea.
Current opinion in neurology
|November 5, 2024
概括
人工智能和机器学习有助于诊断和预测有意识障碍的患者的结果. 预计深度学习技术将进一步增强这些AI/ML模型,以改善临床决策.
科学领域:
- 神经科学是一个神经科学.
- 医疗信息学 医疗信息学
背景情况:
- 在获得脑损伤后严重的意识障碍 (DoC) 带来了诊断和预后挑战.
- 人工智能 (AI) 和机器学习 (ML) 为改善患者护理提供了有希望的工具.
研究的目的:
- 审查使用人工智能和机器学习的最新研究,以减少医院诊断和预后不确定性.
- 用AI/ML来描述患者对新型治疗干预措施的反应.
主要方法:
- 使用AI/ML分析功能神经成像和脑电图 (EEG) 数据.
- 应用传统的机器学习和深度学习算法.
- 使用格拉斯哥结果量表来预测结果.
主要成果:
- AI/ML有效地区分不响应的清醒综合征和最小意识状态.
- 机器学习模型预测患者的结果和对皮和tDCS等干预措施的反应.
- 大多数研究都使用传统的ML来进行深度学习分析.
结论:
- 人工智能和机器学习是DoC诊断,预后和治疗中临床决策的宝贵工具.
- 深度学习技术具有显著的潜力,可以提高AI/ML模型在DoC管理中的性能.
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