Ontology-guided machine learning outperforms zero-shot foundation models for cardiac ultrasound text reports.

ABSTRACT: Big data can revolutionize research and quality improvement for cardiac ultrasound. Text reports are a critical part of such analyses. Cardiac ultrasound reports include structured and free text and vary across institutions, hampering attempts to mine text for useful insights. Natural language processing (NLP) can help and includes both statistical- and large language model based techniques. We tested whether we could use NLP to map cardiac ultrasound text to a three-level hierarchical ontology. We used statistical machine learning (EchoMap) and zero-shot inference using GPT. We tested eight datasets from 24 different institutions and compared both methods against clinician-scored ground truth. Despite all adhering to clinical guidelines, institutions differed in their structured reporting

SUBMITTER: Subramaniam S 

PROVIDER: S-EPMC11828978 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature