Abstract
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.
Abstract (translated)
肺栓塞（PE）是可预防的住院死亡主要原因之一。诊断、风险分层和预防方面的进步可以改善治疗效果。然而，包含PE标签以供研究的大规模公开可用的数据集很少。我们利用MIMIC-IV数据库提取了所有可用的计算机断层扫描肺血管造影（CTPA）放射报告，并由两名医生手动将结果标注为PE阳性（急性PE）或PE阴性。随后，我们将先前微调过的Bio_ClinicalBERT语言模型VTE-BERT应用于自动提取标签。我们通过测量其相对于人工裁定的性能来验证VTE-BERT的可靠性。此外，还将VTE-BERT的表现与诊断代码进行了比较。研究结果表明，在所有19,942例带有CTPA放射报告的急诊科和/或住院患者中，VTE-BERT具有92.4%的敏感性和87.8%的阳性预测值（PPV）。相比之下，在包含11,990名出院诊断代码的住院病人子集中，诊断代码的敏感性为95.4%，而PPV为83.8%。我们成功地在公开可用的数据集中添加了近20,000个CTPA标签，并证明了一种半监督语言模型在外科血液学研究中的加速作用及其外部有效性。
URL
https://arxiv.org/abs/2411.00044
