从信号到因果：一种用于稳健且可解释的临床风险预测的因果信号处理框架
From Signals to Causes: A Causal Signal Processing Framework for Robust and Interpretable Clinical Risk Prediction
AI总结：
本文提出一种因果信号处理框架，通过整合因果建模与神经符号推理，提升临床风险预测的稳健性和可解释性。
摘要：
基于信号处理的系统越来越多地利用异构生物医学信号（包括医学图像、生理时间序列和临床记录）来支持高风险医疗决策。尽管具有强大的预测性能，许多模型依赖于统计相关性，这些相关性在获取设置、患者群体和机构实践之间不稳定，从而限制了鲁棒性、可解释性和临床信任。我们倡导一种因果信号处理的观点，其中生物医学信号被视为潜在生成机制的效果，而不是孤立的预测输入。以临床风险预测为例，我们展示了疾病相关因素如何生成可观察的生物标志物，而获取过程则作为混杂因素影响信号的出现。在从胸部CT扫描和患者风险因素进行临床疾病风险预测时，相关模型可能在扫描器变化下失效，而因果抽象保持不变。基于这一观点，我们提出了一种整合因果建模与基于学习的信号处理和神经符号推理的统一概念框架。统计模型提取多模态表示，这些表示被映射到可解释的因果抽象，并与符号知识编码临床风险因素和指南相结合。这种结构使临床基础的解释、对假设干预的反事实推理以及改进对获取条件或筛查政策变化引起的分布偏移的鲁棒性成为可能。而不是引入特定算法，本文提出了示意性的因果结构，并对基于相关性、因果和神经符号的方法进行了比较分析，以指导设计稳健且可解释的医疗决策支持系统。
英文摘要：
Learning-based signal processing systems increasingly support high-stakes medical decisions using heterogeneous biomedical signals, including medical images, physiological time series, and clinical records. Despite strong predictive performance, many models rely on statistical correlations that are unstable across acquisition settings, patient populations, and institutional practices, limiting robustness, interpretability, and clinical trust. We advocate a causal signal processing perspective in which biomedical signals are treated as effects of latent generative mechanisms rather than as isolated predictive inputs. Using clinical risk prediction as a motivating example, we show how disease-related factors generate observable biomarkers, while acquisition processes act as confounders influencing signal appearance. In clinical disease risk prediction from chest CT scans and patient risk factors, correlational models may fail under scanner changes, whereas causal abstractions remain invariant. Building on this view, we propose a unifying conceptual framework integrating causal modeling with learning-based signal processing and neuro-symbolic reasoning. Statistical models extract multimodal representations that are mapped to interpretable causal abstractions and combined with symbolic knowledge encoding clinical risk factors and guidelines. This structure enables clinically grounded explanations, counterfactual reasoning about hypothetical interventions, and improved robustness to distribution shifts arising from changes in acquisition conditions or screening policies. Rather than introducing a specific algorithm, this article presents schematic causal structures and a comparative analysis of correlation-based, causal, and neuro-symbolic approaches to guide the design of robust and interpretable medical decision-support systems.
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