LLM-AIx: An open source pipeline for Information Extraction from unstructured medical text based on privacy preserving Large Language Models.

ABSTRACT: In clinical science and practice, text data, such as clinical letters or procedure reports, is stored in an unstructured way. This type of data is not a quantifiable resource for any kind of quantitative investigations and any manual review or structured information retrieval is time-consuming and costly. The capabilities of Large Language Models (LLMs) mark a paradigm shift in natural language processing and offer new possibilities for structured Information Extraction (IE) from medical free text. This protocol describes a workflow for LLM based information extraction (LLM-AIx), enabling extraction of predefined entities from unstructured text using privacy preserving LLMs. By converting unstructured clinical text into structured data, LLM-AIx addresses a critical barrier in clinical rese

SUBMITTER: Wiest IC 

PROVIDER: S-EPMC11398444 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature