AI 中文总结
针对不完整临床EHR数据的急诊分诊挑战，本文提出CRS-Triage模型，通过分模态评估可靠性并结合一致性估计置信度，权衡风险与覆盖，在MIMIC-IV-ED数据集上表现优异。
AI 中文摘要
急诊分诊需要在短时间内做出可靠决策，但可用的电子健康记录（EHR）数据（包括结构化数据和临床文本）往往存在不完整、不可靠和不一致的问题，这使得基于机器学习（ML）的分诊预测更具挑战性，因为现有ML模型通常依赖完整且可靠的EHR数据来准确预测患者的病情严重程度。为解决该问题，本文提出置信度与可靠性感知的选择性分诊（CRS-Triage），用于预测患者病情严重程度并给出置信度得分。通过将置信度得分与预定义阈值比较，CRS-Triage可选择性确定模型是做出决策还是推迟处理该病例。具体而言，CRS-Triage分别评估结构化数据和临床文本的可靠性，再结合两种模态间的一致性来估计每次预测的置信度。此外，为降低漏诊（低危分诊）的风险，CRS-Triage通过惩罚低危分诊错误，倾向于为患者分配略高的病情严重程度（即过度分诊）。在MIMIC-IV-ED数据集上的实验表明，CRS-Triage具有出色的预测性能，还能实现更优的风险-覆盖权衡，且在可用EHR数据不完整、质量下降或跨模态不一致时仍保持可靠。
英文摘要
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.
