Introducing mCODEGPT as a zero-shot information extraction from clinical free text data tool for cancer research.

ABSTRACT:

Background

The vast amount of natural language clinical notes about patients with cancer presents a challenge for efficient information extraction, standardization, and structuring. Traditional NLP methods require extensive annotation by domain experts for each type of named entity and necessitate model training, highlighting the need for an efficient and accurate extraction method.

Methods

This study introduces a tool based on the Large Language Model (LLM) for zero-shot information extraction from cancer-related clinical notes into structured data aligned with the minimal Common Oncology Data Elements (mCODE™) structure. We utilize the zero-shot learning capabilities of LLMs for information extraction, eliminating the need for data annotated by domain experts for training.

SUBMITTER: Zhang K 

PROVIDER: S-EPMC12528503 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature