克拉鲁斯:一个交互式可解释的人工智能平台,用于图形神经网络中的手动反事实
Jacqueline Michelle Metsch1, Anna Saranti2, Alessa Angerschmid2
1Institute for Medical Informatics, University Medicalcenter Göttingen, Germany.
Journal of biomedical informatics
|February 1, 2024
概括
克拉鲁斯平台增强了对临床决策支持系统 (CDSS) 人工智能 (AI) 的信任. 它提供交互式可解释AI (XAI),帮助临床医生理解和修改AI预测,以便更好地照顾患者.
科学领域:
- 医疗信息学 医疗信息学
- 计算生物学 计算生物学
- 人工智能的人工智能
背景情况:
- 对人工智能 (AI) 的信任是其在临床决策支持系统 (CDSS) 中采用的主要障碍.
- 人工智能模型的"黑盒子"性质阻碍了医生理解患者特定建议.
- 可解释AI (XAI) 旨在为领域专家澄清AI决策过程,但临床应用需要因果理解.
研究的目的:
- 开发一个交互式平台,CLARUS,以提高临床医生对图形神经网络 (GNN) 预测的理解.
- 为了实现患者特定网络的交互式探索和修改以及人工智能模型再培训.
- 通过人工智能预测的手动反事实分析来促进因果推理.
主要方法:
- 开发CLARUS平台以可视化患者特异性网络和基因/相互作用相关性值.
- 集成可解释AI (XAI) 方法,包括GNNExplainer,用于特征归属.
- 实现交互功能,允许用户改变网络,重新预测结果,并重新训练GNN模型.
主要成果:
- CLARUS提供了患者特定网络的可视化和来自XAI方法的相关性值.
- 该平台可实现患者网络的交互式改变,从而导致GNN模型的重新预测和再培训.
- 用户可以提出和分析手动反事实问题,以了解网络变化对预测的影响.
结论:
- CLARUS被介绍为GNN在临床环境中的第一个交互式XAI平台原型.
- 该平台支持评估反事实问题和基于用户定义的网络更改的再培训模型.
- CLARUS旨在促进对人工智能驱动的医疗决策支持的更深入的临床理解和信任.
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