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通过解释性培训,提高新生儿疼痛评估透明度,举例识别
Imran Hossain1, Ghada Zamzmi1, Peter Mouton2
1Computer Science and Engineering, University of South Florida, Tampa, Florida, USA.
概括
这项研究通过可视化光谱图和解释深度学习决策来提高使用音频信号的新生儿疼痛评估. 它识别和删除无助的训练数据,提高模型的准确性和可靠性在临床环境中.
科学领域:
- 医疗信息学 医疗信息学
- 信号处理 信号处理
- 人工智能的人工智能
背景情况:
- 视觉数据封闭在新生儿疼痛评估中很常见.
- 当视觉数据不可用时,音频信号为疼痛检测提供了一个替代方案.
- 目前用于基于音频的疼痛评估的深度学习 (DL) 方法缺乏透明度.
研究的目的:
- 从音频信号开发一种可解释的DL方法来检测新生儿疼痛.
- 提高用户对自动化疼痛评估系统的信任.
- 识别和减轻无助培训数据的影响.
主要方法:
- 视觉化新生儿音频信号作为光谱图像用于疼痛分类.
- 实施基于实例的方法来解释黑盒DL模型决策.
- 使用影响评分来分析和删除有害的训练实例.
主要成果:
- 拟议的方法成功地可视化音频信号用于疼痛分类.
- 基于DL的疼痛检测增加了可解释性,增强了用户的信心.
- 检测并删除了有害的训练实例,从而获得了压缩数据集和改进的预测.
结论:
- 该方法提供了一种透明和可解释的方法,用于使用音频进行新生儿疼痛评估.
- 这种技术可以增强对人工智能驱动的疼痛检测系统的临床信任.
- 该研究提供了一种可行的策略,用于改善现实世界临床环境中的疼痛评估.
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