MIMIC-IV-Ext-PE: Using a large language model to predict pulmonary embolism phenotype in the MIMIC-IV dataset

• URL: http://arxiv.org/abs/2411.00044v1
• Date: Tue, 29 Oct 2024 19:28:44 GMT
• Title: MIMIC-IV-Ext-PE: Using a large language model to predict pulmonary embolism phenotype in the MIMIC-IV dataset
• Authors: B. D. Lam, S. Ma, I. Kovalenko, P. Wang, O. Jafari, A. Li, S. Horng,
• Abstract summary: Pulmonary embolism is a leading cause of preventable in-hospital mortality. There are few large publicly available datasets that contain PE labels for research. We extracted all available radiology reports ofCTPA scans and two physicians manually labeled the results as PE positive (acute PE) or PE negative. We applied a previously finetuned Bio_ClinicalBERT transformer language model, VTE-BERT, to extract labels automatically.
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• 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.

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