这篇论文做了个急诊分诊模型，数据残缺时也能给预测打个置信分，拿不准就推迟判断，还用MIMIC-IV-ED实测过，挺实在的。
CRS-Triage是一个面向急诊分诊的机器学习模型，能在电子健康档案数据不完整、不可靠或不一致时预测患者 acuity 等级并给出置信度。模型分别评估结构化数据和临床文本的可靠性，再联合两模态一致性来估计预测置信度。通过设定阈值，模型可选择性决定是自行决策还是延迟处理，以减少高风险患者漏诊。在 MIMIC-IV-ED 数据集上，CRS-Triage 表现出强预测性能，并提供更优的风险-覆盖率权衡。
CRS-Triage: Confidence- and Reliability-Aware Selective Triage under Incomplete Clinical Evidence
Emergency triage requires reliable decisions within a short time period. However, the available electronic health record (EHR) data, including structured data and clinical text, are often incomplete, unreliable, and inconsistent. This makes machine learning (ML)-based triage prediction more challenging, as existing ML models typically rely on complete and reliable EHR data to accurately predict patients' acuity levels. To address this, we propose confidence- and reliability-aware selective triage (CRS-Triage) to predict patients' acuity levels with a confidence score. By comparing the confidence score with a predefined threshold, CRS-Triage can selectively determine whether the model should make the decision or defer the case. Specifically, CRS-Triage separately evaluates the reliability of structured data and clinical text and then jointly considers the consistency between the two modalities to estimate the confidence of each prediction. Moreover, to reduce the risk of missing high-acuity patients, namely under-triage, CRS-Triage prefers to assign patients slightly higher acuity levels, namely over-triage, by penalizing under-triage errors. Experiments on the MIMIC-IV-ED dataset show that CRS-Triage achieves strong predictive performance. It also provides a better risk-coverage trade-off and remains reliable when the available EHR data are incomplete, degraded, or inconsistent across modalities.