Pulmonary embolism (PE) is a leading cause of preventable in-hospital mortality. Advances in diagnosis, risk stratification, and prevention can improve outcomes. There are few large publicly available datasets that contain PE labels for research. Using the MIMIC-IV database, we extracted all available radiology reports of computed tomography pulmonary angiography (CTPA) scans and two physicians manually labeled the results as PE positive (acute PE) or PE negative. We then applied a previously finetuned Bio_ClinicalBERT transformer language model, VTE-BERT, to extract labels automatically. We verified VTE-BERT's reliability by measuring its performance against manual adjudication. We also compared the performance of VTE-BERT to diagnosis codes. We found that VTE-BERT has a sensitivity of 92.4% and positive predictive value (PPV) of 87.8% on all 19,942 patients with CTPA radiology reports from the emergency room and/or hospital admission. In contrast, diagnosis codes have a sensitivity of 95.4% and PPV of 83.8% on the subset of 11,990 hospitalized patients with discharge diagnosis codes. We successfully add nearly 20,000 labels to CTPAs in a publicly available dataset and demonstrate the external validity of a semi-supervised language model in accelerating hematologic research.

本研究针对肺栓塞（PE）这一常见且可预防的医院内死亡原因，填补了公共数据库中PE标签不足的空白。研究采用经过微调的Bio_ClinicalBERT语言模型VTE-BERT自动提取PE标签，发现其对CTPA影像报告的敏感度为92.4%，且与传统诊断代码相比展现了较高的外部有效性，结果为促进血液学研究提供了新的数据支持。

利用大型语言模型预测MIMIC-IV数据集中肺栓塞表型的研究