Development and Validation of VTE-BERT Natural Language Processing Model for Venous Thromboembolism.

ABSTRACT:

Background

Accurate and rapid phenotyping of venous thromboembolism (VTE) in longitudinal studies is important. A natural language processing (NLP) tool externally validated in representative patients is lacking.

Methods

We designed a novel NLP platform, NLPMed, to assist thrombosis researchers with data preprocessing, phenotype annotation, language model finetuning, and NLP application. Utilizing clinical notes, discharge summaries, and radiology reports in patients with cancer from two healthcare institutions, we finetuned Bio_ClinicalBERT to develop VTE-BERT. The new model was trained to detect acute VTE events and their anatomical locations longitudinally. We internally and externally validated the model's performance in two randomly sampled cohorts of patients with adva

SUBMITTER: Jafari O 

PROVIDER: S-EPMC12360494 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature