Validation of large language models for detecting pathologic complete response in breast cancer using population-based pathology reports.

ABSTRACT:

Aims

The primary goal of this study is to evaluate the capabilities of Large Language Models (LLMs) in understanding and processing complex medical documentation. We chose to focus on the identification of pathologic complete response (pCR) in narrative pathology reports. This approach aims to contribute to the advancement of comprehensive reporting, health research, and public health surveillance, thereby enhancing patient care and breast cancer management strategies.

Methods

The study utilized two analytical pipelines, developed with open-source LLMs within the healthcare system's computing environment. First, we extracted embeddings from pathology reports using 15 different transformer-based models and then employed logistic regression on these embeddings to classify the

SUBMITTER: Cheligeer K 

PROVIDER: S-EPMC11447988 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature