量化临床决策中的症状因果关系：基于CausaLM的探索
Quantifying Symptom Causality in Clinical Decision Making: An Exploration Using CausaLM
AI总结：
本研究利用CausaLM框架和TReATE方法，量化“胸痛”症状对临床NLP模型诊断预测的因果影响，超越相关性分析，为更可信、可解释的医疗决策支持工具提供依据。
摘要：
当前的机器学习方法在医学诊断中往往依赖于症状与疾病之间的相关性模式，当症状模糊或在多种疾病中常见时，可能导致误诊。在本工作中，我们超越相关性，研究关键症状——特别是“胸痛”——对诊断预测的因果影响。利用CausaLM框架，我们生成反事实文本表示，其中目标概念被有效“遗忘”，从而能够对概念对模型预测疾病分布的因果效应进行原则性估计。通过采用基于文本表示的平均处理效应（TReATE），我们量化了症状存在或缺失如何影响模型的诊断结果，并将这些发现与基于相关性的基线（如CONEXP）进行对比。我们的结果提供了对临床NLP模型决策行为的更深入洞察，并有可能为医疗实践中更可信、可解释且基于因果的决策支持工具提供信息。
英文摘要：
Current machine learning approaches to medical diagnosis often rely on correlational patterns between symptoms and diseases, risking misdiagnoses when symptoms are ambiguous or common across multiple conditions. In this work, we move beyond correlation to investigate the causal influence of key symptoms-specifically "chest pain" on diagnostic predictions. Leveraging the CausaLM framework, we generate counterfactual text representations in which target concepts are effectively "forgotten" enabling a principled estimation of the causal effect of that concept on a model's predicted disease distribution. By employing Textual Representation-based Average Treatment Effect (TReATE), we quantify how the presence or absence of a symptom shapes the model's diagnostic outcomes, and contrast these findings against correlation-based baselines such as CONEXP. Our results offer deeper insight into the decision-making behavior of clinical NLP models and have the potential to inform more trustworthy, interpretable, and causally-grounded decision support tools in medical practice.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
