Health AI Central
Peer-Reviewed Publication
Support Care Cancer2026;34(8)July 29, 2026Journal Article

Machine learning approaches to predict early cardiac immune-related adverse events in patients receiving immune checkpoint inhibitors.

Michael Sayer1, Peter D Chang2,3, Hirofumi Hamano4, Reina Yamamoto5, Misako Nagasaka6, Ali A Naqvi7, Pranav M Patel7, Yoshito Zamami4, Aya F Ozaki8
1School of Pharmacy & Pharmaceutical Sciences, University of California, Irvine, CA, USA.
2School of Medicine, Pathology & Laboratory Medicine, University of California, Irvine, CA, USA.
3Donald Bren School of Information and Computer Sciences, University of California, Irvine, CA, USA.
4Department of Pharmacy, Medical Development Field, Okayama University, Okayama, Japan.
5Department of Medicinal Pharmacology, Graduate School of Medicine, Dentistry, and Pharmaceutical Sciences, Okayama University, Okayama, Japan.
6Division of Hematology and Oncology, University of California, Irvine, CA, USA.
7Mary & Steve Wen Cardiovascular Division, Department of Medicine, University of California, Irvine, CA, USA.
8School of Pharmacy & Pharmaceutical Sciences, University of California, Irvine, CA, USA. afozaki@uci.edu.
PubMed DOI

Abstract

PURPOSE: Immune checkpoint inhibitor (ICI)-induced cardiac immune-related adverse events (cardiac irAEs) are rare yet serious complications. Clinical assessment tools to identify at-risk patients would allow for more effective prevention strategies, thus improving clinical outcomes. We constructed various machine learning (ML) models to predict these events among patients receiving ICI therapy. M…

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