量化西班牙医疗记录中诊断的可解释的自动分类的决策支持水平
Nuria Lebeña1, Alicia Pérez2, Arantza Casillas1
1HiTZ Center - Ixa, Department of Electricity and Electronics, University of the Basque Country (UPV/EHU), Barrio Sarriena 2, Leioa 48940, Spain.
Computers in biology and medicine
|September 13, 2024
概括
本研究介绍了西班牙电子健康记录 (EHR) 分类的可解释AI,改进了决策支持. 与SHAP和集成梯度 (IG) 相比,局部可解释的模型不可知解释 (LIME) 显示出更好的解释性.
科学领域:
- 医疗保健中的人工智能
- 临床信息学 临床信息学
- 自然语言处理自然语言处理.
背景情况:
- 自动电子健康记录 (EHR) 的分类缺乏非黑子方法,特别是西班牙语临床文本.
- 现有的可解释性方法缺乏标准化指标来评估决策支持.
- 需要在临床语言分类中使用可解释的AI来进行更好的决策.
研究的目的:
- 使用可解释的方法对西班牙电子健康记录 (EHR) 进行分类,以加强决策支持.
- 提出Leberage,一种用于量化可解释预测的决策支持水平的新指标.
- 评估夏普利添加式解释 (SHAP),局部可解释模型不可知解释 (LIME) 和集成梯度 (IG) 的解释能力.
主要方法:
- 开发了一个基于longformer的系统来处理长长的EHR文档.
- 应用了独立于模型的可解释性技术 (SHAP,LIME,IG) 来提取激发ICD代码的文本段.
- 提出并实施了Leberage度量来衡量可解释性结果.
主要成果:
- 在EHR分类任务性能方面取得了7%的改进.
- 与集成梯度 (IG) 和夏普利添加式解释 (SHAP) 相比,局部可解释的模型不可知解释 (LIME) 的解释性更强.
- 建议的勒贝拉奇度量有效量化了可解释性技术的贡献.
结论:
- 探索的可解释性技术在澄清复杂模型的输出方面是有效的,例如长型模型.
- 新的Leberage度量是评估临床应用中可解释性质量的宝贵工具.
- 这项研究在西班牙EHR分类的背景下推进了可解释的AI.
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