在医疗保健中使用模式发现和解的基准分析解释性
Pei-Yuan Zhou1, Amane Takeuchi2, Fernando Martinez-Lopez3
1System Design Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada.
Bioengineering (Basel, Switzerland)
|March 28, 2025
概括
本研究介绍了模式发现和解 (PDD) 系统,这是一个不受监督的AI算法,可以从临床笔记中提供可解释的见解. PDD帮助医疗保健从业人员了解AI决策,并协助疾病诊断.
科学领域:
- 医疗保健中的人工智能
- 临床信息学 临床信息学
- 机器学习用于医学.
背景情况:
- 医疗保健系统越来越多地使用人工智能,但它们的"黑子"性质阻碍了从业者的信任和理解.
- 解释AI决策对于安全有效的临床整合至关重要.
研究的目的:
- 为了对临床笔记分析的无监督学习算法进行模式发现和解 (PDD) 的基准测试.
- 评估PDD提供可解释输出和帮助医疗保健决策的能力.
- 将PDD的性能和可解释性与监督深度学习模型和后期可解释性技术进行比较.
主要方法:
- 利用MIMIC-IV数据集,使用术语频率-反向文档频率和主题建模处理临床笔记和ICD-9代码.
- 应用了PDD算法用于特征离散,在解散的统计空间中发现模式,以及临床记录集群.
- 与PDD的整体可解释性比较的后期可解释性方法 (特征变换,梯度SHAP,集成梯度).
主要成果:
- 在PDD中,无监督集群的性能与监督深度学习模型相美.
- PDD算法生成了一个可解释的知识库,将临床数据,模式和知识联系起来.
- 与PDD的整体解释性相比,post-hoc解释性技术在临床诊断方面存在局限性.
结论:
- 在临床环境中,PDD提供了一种可行的解决方案,以提高AI的解释性.
- 该系统的全球可解释性有助于从业人员了解AI决策过程.
- PDD有效地聚类疾病,提供有价值的见解来支持临床诊断.
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