一种基于人工智能的创新方法,将复杂的模型压缩成可解释的,与模型无关的和减少的决策支持系统,适用于医疗保健 (NEAR)
Karim Kassem1, Michela Sperti1, Andrea Cavallo2
1Polito(BIO)Med Lab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Turin, Italy.
Artificial intelligence in medicine
|April 24, 2024
概括
这项研究介绍了基于人工智能的系统NEAR,该系统使临床决策支持可解释和个性化. NEAR 实现了与复杂的人工智能模型相似的性能,同时为更好的患者护理提供可解释的见解.
科学领域:
- 医疗保健中的人工智能
- 临床决策支持系统 临床决策支持系统
- 精准医学是一门精准的医学.
背景情况:
- 当前的临床指导方针往往是静态的,缺乏个性化.
- 人工智能 (AI) 为个性化医疗提供了潜力,但由于其"黑子"性质,它面临着采用挑战.
- 在临床决策支持系统 (CDSS) 中需要可解释的AI.
研究的目的:
- 介绍和验证NEAR,一种基于AI的创新方法,用于将AI预测模型压缩成可解释的,与模型无关的CDSS.
- 在临床实践中解决当前基于指南的系统和"黑子"AI算法的局限性.
- 为了实现个性化,以患者为导向的医疗决策.
主要方法:
- NEAR使用Shapley增量解释框架来确定特征对AI模型输出的贡献.
- 它使用自定义库来近似和合并功能贡献,创建简化,可解释的模型.
- 该系统估计了可信度误差,以提高可解释性,并根据急性冠状动脉综合征 (ACS) 死亡率预测进行了评估.
主要成果:
- 在ACS用例中,NEAR表现出与原始人工智能模型 (如自适应增强分类器) 相比的性能.
- 对于NEAR的曲线下的面积 (AUC) 从统计学上来说与原始模型没有差异,这表明简化后的准确性得到保留.
- NEAR提供了内在的可解释性和模块化,具有功能性的Web应用程序平台进行测试.
结论:
- 已经成功开发了一种适用于单个患者分析的可解释和可靠的CDSS.
- NEAR系统可以增强当前的临床指导方针,使其更加个性化和动态.
- 这种基于人工智能的方法有助于临床医生做出明智的日常治疗决策.
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