从不确定性到临床风险：面向交互式医疗诊断的严重程度感知共形规划
From Uncertainty to Clinical Risk: Severity-Aware Conformal Planning for Interactive Medical Diagnosis
- Lanzhou University（兰州大学）
- Hunan University（湖南大学）
- National University of Singapore（新加坡国立大学）
- City University of Hong Kong（香港城市大学）
- The Ohio State University（俄亥俄州立大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究针对交互式医疗诊断漏诊重症的风险与长程规划缺失问题，提出严重程度感知共形临床规划框架，在DDXPlus和MediQ上提升了诊断准确性、鉴别质量并降低了高风险错误。
AI中文摘要：
交互式医疗诊断通过多轮提问动态获取患者信息，可在证据不完整的情况下支持准确、高效且安全的临床决策。现有方法通常基于预测不确定性或标签歧义引导信息获取，但忽略了漏诊重症的不对称临床风险，且缺乏关于是否继续提问或确定诊断的统一长程规划。为解决这些局限，我们提出Severity-Aware Conformal Clinical Planning（严重程度感知共形临床规划），将交互式诊断建模为风险敏感的序列决策问题。该框架维护互补的诊断、安全及掩码证据信念；在保留的诊断轨迹上校准轮次特定的诊断预测集与严重程度加权的鉴别诊断风险；并将校准后的临床风险引入蒙特卡洛树搜索，以联合评估长程的提问（Ask）与确定诊断（Commit）轨迹。在DDXPlus和MediQ上的实验表明，我们的方法在多个大语言模型上用更少的提问实现了更准确的诊断，同时提升了鉴别诊断质量并减少了重症病例中的高风险错误。这些发现验证了使用临床风险而非仅预测不确定性作为规划信号的价值，证明了所提框架在信息获取和风险感知诊断决策中的有效性，也为未来面向临床风险的交互式诊断及信息获取方法的研究提供了动力。
英文摘要：
Interactive medical diagnosis dynamically acquires patient information through multiple rounds of questioning, supporting accurate, efficient, and safe clinical decisions under incomplete evidence. Existing methods commonly guide information acquisition with predictive uncertainty or label ambiguity, but overlook the asymmetric clinical risk of missing severe diseases and lack unified long-horizon planning over whether to continue asking questions or commit to a diagnosis. To address these limitations, we propose Severity-Aware Conformal Clinical Planning, which formulates interactive diagnosis as a risk-sensitive sequential decision problem. The framework maintains complementary diagnostic, safety, and masked-evidence beliefs; calibrates turn-specific diagnostic prediction sets and severity-weighted differential-diagnosis risk on held-out diagnostic trajectories; and introduces the calibrated clinical risk into Monte Carlo Tree Search to jointly evaluate long-horizon Ask and Commit trajectories. Experiments on DDXPlus and MediQ show that our method achieves more accurate diagnoses with fewer questions across multiple large language models, while improving differential-diagnosis quality and reducing high-risk errors in severe cases. These findings validate the value of using clinical risk, rather than predictive uncertainty alone, as a planning signal and demonstrate the effectiveness of the proposed framework for information acquisition and risk-aware diagnostic decision making. They also motivate future work on clinical-risk-oriented interactive diagnosis and information-acquisition methods.
