Medical Feature Extraction From Clinical Examination Notes: Development and Evaluation of a Two-Phase Large Language Model Framework.

ABSTRACT:

Background

Medical feature extraction from clinical text is challenging because of limited data availability, variability in medical terminology, and the critical need for trustworthy outputs. Large language models (LLMs) offer promising capabilities but face critical challenges with hallucination.

Objective

This study aims to develop a robust framework for medical feature extraction that enhances accuracy by minimizing the risk of hallucination, even with limited training data.

Methods

We developed a two-phase training approach. Phase 1 used instructing fine-tuning to teach feature extraction. Phase 2 introduced confidence-regularization fine-tuning with loss functions penalizing overconfident incorrect predictions, which were captured using bidirectional matching ta

SUBMITTER: Abumelha M 

PROVIDER: S-EPMC12712565 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature