Automated Extraction of Key Entities from Non-English Mammography Reports Using Named Entity Recognition with Prompt Engineering.

ABSTRACT:

Objective

Named entity recognition (NER) offers a powerful method for automatically extracting key clinical information from text, but current models often lack sufficient support for non-English languages.

Materials and methods

This study investigated a prompt-based NER approach using Google's Gemini 1.5 Pro, a large language model (LLM) with a 1.5-million-token context window. We focused on extracting important clinical entities from Turkish mammography reports, a language with limited available natural language processing (NLP) tools. Our method employed many-shot learning, incorporating 165 examples within a 26,000-token prompt derived from 75 initial reports. We tested the model on a separate set of 85 unannotated reports, concentrating on five key entities: anatomy (AN

SUBMITTER: Akcali Z 

PROVIDER: S-EPMC11852152 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature