Hybrid rule-based and on-premises LLM pipeline for extracting CMR and CPET metrics from free-text reports in repaired tetralogy of Fallot.

ABSTRACT:

Background

Patients with repaired tetralogy of Fallot (rTOF) require lifelong surveillance with cardiovascular magnetic resonance (CMR) and cardiopulmonary exercise testing (CPET). However, results are frequently stored as unstructured free-text reports, hindering large-scale analysis and research.

Objectives

This study aimed to develop and evaluate a privacy-preserving hybrid natural language processing pipeline combining regular expressions (regex) and an on-premises large language model (LLM) to accurately extract key CMR and CPET metrics from legacy free-text reports in patients with rTOF.

Methods

We retrospectively analyzed 430 CMR and 262 CPET reports (2005-2023) from patients with rTOF. A two-stage hybrid pipeline was implemented: regex rules were applied first

SUBMITTER: Akbasli IT 

PROVIDER: S-EPMC12870707 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature