LLMs for MACE Detection in Clinical Notes

An end-to-end Generative AI system pipeline designed to detect Major Adverse Cardiac Events (MACE) outcomes in clinical notes, specifically focusing on Renal Cell Carcinoma (RCC) patient records.

This project leverages Large Language Models to automatically identify and extract MACE events from unstructured clinical text, enabling better tracking of adverse cardiac events in oncology patients.

Status: Pending Publication

Technologies: Large Language Models, NLP, Clinical Text Processing, Python

Affiliation: Mayo Clinic, Scottsdale Campus