探索深度学习和可解释AI的潜力和局限性,用于纵向生命过程分析
Helen Coupland1, Neil Scheidwasser1, Alexandros Katsiferis1
1Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark.
BMC public health
|April 24, 2025
概括
深度学习模型可以揭示复杂的生活过程健康模式传统方法错过,特别是稀少的数据. 然而,可解释的AI方法,如SHAP值,可能不能准确地反映真正的因果关系.
科学领域:
- 流行病学 流行病学
- 计算健康科学 计算健康科学
- 人工智能在公共卫生中的作用
背景情况:
- 了解生命周期暴露和疾病风险对于公共卫生干预至关重要.
- 传统的流行病学方法与生命周期数据中的非线性,动态关系作斗争.
- 深度学习 (DL) 和可解释的人工智能 (XAI) 对个性化干预有希望,但存在局限性.
研究的目的:
- 评估DL模型的性能与分析生命周期数据的传统方法相比.
- 评估XAI的实用性,特别是SHAP值,用于解释DL模型的发现.
- 在稀疏的纵向健康数据中确定DL和XAI的挑战和机会.
主要方法:
- 一项受控模拟研究将DL架构 (CNN,RNN) 与XGBoost和物流回归进行了比较.
- 模拟了现实的纵向数据场景,以模仿流行病学概念.
- 用各种指标评估模型性能,包括处理类不平衡,并计算SHAP值.
主要成果:
- DL方法有效地识别了线性和基于树的模型所遗漏的动态关系.
- 与传统方法相比,DL模型在稀疏的纵向数据中表现出优异的性能.
- 尽管预测性表现良好,但SHAP值往往与潜在的因果关系不一致.
结论:
- DL为分析复杂的人生健康数据提供了先进的功能,特别是在稀疏性方面.
- 当前的XAI方法可能无法可靠地反映因果机制,需要进一步开发.
- 未来的研究应该专注于改进个性化公共卫生中DL的解释性框架.
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