Automated natural language processing to identify venous thromboembolism from diagnostic imaging reports.

ABSTRACT: The annual incidence of venous thromboembolism (VTE) may be 50-fold increased after allogeneic hematopoietic stem cell transplant (HSCT). Such incidence data, as well as data that establish clinical variables resulting in this enhanced risk, have generally required manual chart review. This cumbersome process can be improved by natural language processing (NLP) algorithms designed to detect VTE in electronic medical record systems. We describe the development of an institutional NLP algorithm for VTE detection, and our evaluation of its performance in detecting VTE in patients who recently underwent HSCT. We retrospectively reviewed adult patients between 2016 and 2020. NLP assessed patient records for acute VTE within 100 days of HSCT, and manual chart review was performed for comparison.

SUBMITTER: Subramanian N 

PROVIDER: S-EPMC12768905 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature