Deriving Real-World Evidence from Non-English Electronic Medical Records in Hormone Receptor-Positive Breast Cancer Using Large Language Models.

ABSTRACT: Background/Objectives: Large language models (LLMs) have been proposed as a means of converting unstructured electronic medical records (EMRs) into structured datasets. However, concerns regarding the reliability of these models in non-English clinical text and their capacity to generate novel insights remain unresolved. We aimed to utilize an LLM to identify a hypothetical "Luminal B poor-prognosis" breast cancer subgroup (LPP) based on progesterone receptor (PR), the Ki-67 proliferation index, and grade characteristics, while concurrently validating the LLM's accuracy. Methods: We retrospectively compiled the EMRs on 7756 female breast cancer patients from five Moscow oncology centers. An LLM with a domain-engineered prompt extracted eight clinicopathological variables (Ki-

SUBMITTER: Meretukov D 

PROVIDER: S-EPMC12691197 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature