Natural Language Processing framework for identifying abdominal aortic aneurysm repairs using unstructured electronic health records.

ABSTRACT: Patient identification for national registries often relies upon clinician recognition of cases or retrospective searches using potentially inaccurate clinical codes, leading to incomplete data capture and inefficiencies. Natural Language Processing (NLP) offers a promising solution by automating analysis of electronic health records (EHRs). This study aimed to develop NLP models for identifying and classifying abdominal aortic aneurysm (AAA) repairs from unstructured EHRs, demonstrating a proof-of-concept for automated patient identification in registries like the National Vascular Registry. Using the MIMIC-IV-Note dataset, a multi-tiered approach was developed to identify vascular patients (Task 1), AAA repairs (Task 2), and classify repairs as primary or revision (Task 3). Four NLP mode

SUBMITTER: Thompson DC 

PROVIDER: S-EPMC12280078 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature