基于临床大语言模型的文本与结构化数据融合型急诊分诊预测模型研究OA

Objective To develop an emergency triage prediction model based on a clinical large language model by inte-grating unstructured clinical text with structured triage information,so as to facilitate the early prediction of hospitalization and critical outcomes in emergency department(ED)patients.Methods The diagnostic text information and data on age,gender and six vital signs were extracted based on the adult emergency department visit records from the MIMIC-Ⅳ-ED database,including body temperature,heart rate,respiratory rate,oxygen saturation,systolic blood pressure and diastolic blood pressure.The semantic features from the diagnostic texts were encoded with the ClinicalBERT large language model,and then integrated with the structured triage information to build a ClinFusion predictive model.The ClinFusion predictive model had its performance verified by the comparison with various traditional triage systems and machine learning models within a unified evaluation framework.Results In the hospitalization prediction task,the ClinFusion predictive model achieved an area under the receiver operating characteristic curve(AUROC)of 0.896 and an area under the precision-recall curve(AUPRC)of 0.885,outperforming the best-performing machine learning model of multilayer perceptron(MLP)with an AUROC of 0.820 and an AUPRC of 0.791 and the best-performing traditional triage system of emergency severity index(ESI)with an AUROC of 0.708 and an AUPRC of 0.628.In the critical outcome prediction task,the model achieved an AUROC of 0.915 and an AUPRC of 0.541,again outperforming the best-performing machine learning model of gradient boosting(GB)with an AUROC of 0.881 and an AUPRC of 0.398,and the best-performing traditional triage system of ESI with an AUROC of 0.806 and an AUPRC of 0.199.Conclusion The ClinFusion model integrating clinical text with structured triage data significantly improves the ability to predict emergency patients'need for hospitalization and critical outcomes at an early stage,offering an efficient and feasible technical solution for intelligent emergency triage and clinical decision support.[Chinese Medical Equipment Journal,2026,47(5):1-10]