Diagnosis extraction from unstructured Dutch echocardiogram reports using span- and document-level characteristic classification.

ABSTRACT:

Background

Clinical machine learning research and artificial intelligence driven clinical decision support models rely on clinically accurate labels. Manually extracting these labels with the help of clinical specialists is often time-consuming and expensive. This study tests the feasibility of automatic span- and document-level diagnosis extraction from unstructured Dutch echocardiogram reports.

Methods

We included 115,692 unstructured echocardiogram reports from the University Medical Center Utrecht, a large university hospital in the Netherlands. A randomly selected subset was manually annotated for the occurrence and severity of eleven commonly described cardiac characteristics. We developed and tested several automatic labelling techniques at both span and document leve

SUBMITTER: Arends B 

PROVIDER: S-EPMC11887187 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature