LLMs for MACE Detection in Clinical Notes
Jul 1, 2025·
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1 min read
Naif A. Ganadily
Abstract
An end-to-end Generative AI system pipeline to detect Major Adverse Cardiac Events (MACE) outcomes in clinical notes, specifically for Renal Cell Carcinoma (RCC) patient records. This work leverages Large Language Models to automatically identify and extract MACE events from unstructured clinical text.
Type
Status: Pending Publication
This research develops an end-to-end Generative AI system pipeline to automatically detect Major Adverse Cardiac Events (MACE) in clinical notes from oncology patients, with a focus on Renal Cell Carcinoma (RCC) cases.
Affiliation: Mayo Clinic, Scottsdale Campus & Arizona State University
