提出混合框架，提升西语病历中艾滋病疑诊的可靠自动分诊。
Reliable Automated Triage in Spanish Clinical Notes: A Hybrid Framework for Risk-Aware HIV Suspicion Identification
- 融合概率与几何双重验证，分离认知与随机不确定性。
- 在严格可靠性约束下仍保持高覆盖率，避免传统方法的覆盖坍塌。
- 适合医疗风险敏感场景，尤其适用于临床决策支持系统。
标准临床自然语言处理基准常因对模糊实例强制确定性分类而产生虚高指标，掩盖了过度自信预测的临床风险。为此，我们提出一种风险感知的混合选择性分类框架，用于西班牙语临床笔记中的早期人类免疫缺陷病毒（HIV）疑诊识别。采用双重验证策略，通过Mondrian conformal prediction解耦随机不确定性，并利用多中心马氏距离投票机制捕捉认知不确定性。实证评估表明，标准不确定性度量和基线分类器在严格可靠性约束下结构上不足，导致严重覆盖坍塌。相比之下，通过要求临床文本同时通过概率与几何双重保障，所提框架成功隔离出高度可信的操作域。
原文摘要 · Abstract (English)
Standard clinical Natural Language Processing (NLP) benchmarks often yield inflated metrics by forcing deterministic classification on ambiguous instances, thereby obscuring the clinical risks of overconfident predictions. To bridge this gap, we propose a risk-aware hybrid selective classification framework, evaluated on early Human Immunodeficiency Virus suspicion identification in Spanish clinical notes. Our dual-verification approach explicitly decouples aleatoric uncertainty through Mondrian conformal prediction and epistemic uncertainty using a Multi-Centroid Mahalanobis Distance veto. Empirical evaluations reveal that standard uncertainty metrics and baseline classifiers are structurally insufficient for safe medical triage, suffering severe coverage collapse when forced to operate under strict reliability constraints. In contrast, by demanding that clinical narratives pass both probabilistic and geometric safeguards, the proposed framework successfully isolates a highly trustworthy operational domain.
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