让医疗预测模型可解释且可交互，医生能参与并理解诊断逻辑。
No Black Boxes: Interpretable and Interactable Predictive Healthcare with Knowledge-Enhanced Agentic Causal Discovery
- 用知识增强的智能体框架挖掘因果关系，支持医生自定义输入。
- 在MIMIC-III和MIMIC-IV数据集上表现更优，可解释性显著提升。
- 适合需要透明决策过程的临床医生与医疗AI研究者使用。
基于大规模电子健康记录（EHR）数据的深度学习模型在疾病预测中已实现高精度，有望辅助临床决策与治疗规划。然而，这些模型缺乏临床医生高度关注的两个关键特性：可解释性与可交互性。其“黑箱”特性使医生难以理解预测依据，限制了判断能力；同时缺乏互动机制，无法融入医生的专业知识与经验。为此，我们提出II-KEA——一种融合个性化知识库与代理型大语言模型的知识增强型因果发现框架。该框架通过显式推理与因果分析提升可解释性，并支持医生通过定制知识库与提示注入自身经验，增强交互性。在MIMIC-III和MIMIC-IV数据集上的评估表明，II-KEA不仅性能优越，且经大量案例研究验证了其强解释性与交互性。
原文摘要 · Abstract (English)
Deep learning models trained on extensive Electronic Health Records (EHR) data have achieved high accuracy in diagnosis prediction, offering the potential to assist clinicians in decision-making and treatment planning. However, these models lack two crucial features that clinicians highly value: interpretability and interactivity. The ``black-box'' nature of these models makes it difficult for clinicians to understand the reasoning behind predictions, limiting their ability to make informed decisions. Additionally, the absence of interactive mechanisms prevents clinicians from incorporating their own knowledge and experience into the decision-making process. To address these limitations, we propose II-KEA, a knowledge-enhanced agent-driven causal discovery framework that integrates personalized knowledge databases and agentic LLMs. II-KEA enhances interpretability through explicit reasoning and causal analysis, while also improving interactivity by allowing clinicians to inject their knowledge and experience through customized knowledge bases and prompts. II-KEA is evaluated on both MIMIC-III and MIMIC-IV, demonstrating superior performance along with enhanced interpretability and interactivity, as evidenced by its strong results from extensive case studies.
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