  Rapid and accurate identification of Venous thromboembolism (VTE), a severe
cardiovascular condition including deep vein thrombosis (DVT) and pulmonary
embolism (PE), is important for effective treatment. Leveraging Natural
Language Processing (NLP) on radiology reports, automated methods have shown
promising advancements in identifying VTE events from retrospective data
cohorts or aiding clinical experts in identifying VTE events from radiology
reports. However, effectively training Deep Learning (DL) and the NLP models is
challenging due to limited labeled medical text data, the complexity and
heterogeneity of radiology reports, and data imbalance. This study proposes
novel method combinations of DL methods, along with data augmentation, adaptive
pre-trained NLP model selection, and a clinical expert NLP rule-based
classifier, to improve the accuracy of VTE identification in unstructured
(free-text) radiology reports. Our experimental results demonstrate the model's
efficacy, achieving an impressive 97\% accuracy and 97\% F1 score in predicting
DVT, and an outstanding 98.3\% accuracy and 98.4\% F1 score in predicting PE.
These findings emphasize the model's robustness and its potential to
significantly contribute to VTE research.
