start-ver=1.4
cd-journal=joma
no-vol=49
cd-vols=
no-issue=1
article-no=
start-page=66
end-page=73
dt-received=
dt-revised=
dt-accepted=
dt-pub-year=2026
dt-pub=20260110
dt-online=
en-article=
kn-article=
en-subject=
kn-subject=
en-title=
kn-title=Exploratory Analysis for Development Predictive Models of Immune Checkpoint Inhibitor-Induced Myocarditis Using a Nationwide Claims Database
en-subtitle=
kn-subtitle=
en-abstract=
kn-abstract=Immune checkpoint inhibitors (ICIs), essential in cancer therapy, can cause severe immune-related adverse events (irAEs), including myocarditis with a high fatality rate. Currently, the pathogenesis, biomarkers, and risk factors of ICI-induced myocarditis (ICIM) are not fully understood. This exploratory study aimed to develop machine learning-based models to predict the onset of ICIM within 3 months of starting ICI therapy, using a large health insurance database. The models were constructed using the Light Gradient Boosting Machine (LightGBM) and Random Forest algorithms, incorporating clinical variables such as comorbidities and prior medication classifications. In this study, a strategy combining undersampling and bagging was used to minimize the impact of highly imbalanced datasets. The Random Forest model demonstrated superior performance compared with the LightGBM model, and the SHapley Additive exPlanations (SHAP) analysis for the Random Forest model revealed that the concurrent use of ICIs was the most important variable for predictions. Although predictive performance remains limited (AUROC ≈ 0.63), this exploratory framework demonstrates the feasibility of developing data-driven risk prediction models for ICIM. Future studies with expanded datasets and integration of laboratory parameters are warranted to improve predictive accuracy and potential clinical applicability.
en-copyright=
kn-copyright=

en-aut-name=YamamotoReina
en-aut-sei=Yamamoto
en-aut-mei=Reina
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=1
ORCID=

en-aut-name=HamanoHirofumi
en-aut-sei=Hamano
en-aut-mei=Hirofumi
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=2
ORCID=

en-aut-name=NakagomiKoki
en-aut-sei=Nakagomi
en-aut-mei=Koki
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=3
ORCID=

en-aut-name=UchiyamaMiyu
en-aut-sei=Uchiyama
en-aut-mei=Miyu
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=4
ORCID=

en-aut-name=MichiharaAyana
en-aut-sei=Michihara
en-aut-mei=Ayana
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=5
ORCID=

en-aut-name=OzakiAya F.
en-aut-sei=Ozaki
en-aut-mei=Aya F.
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=6
ORCID=

en-aut-name=PatelPranav M.
en-aut-sei=Patel
en-aut-mei=Pranav M.
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=7
ORCID=

en-aut-name=TaniokaMaki
en-aut-sei=Tanioka
en-aut-mei=Maki
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=8
ORCID=

en-aut-name=ZamamiYoshito
en-aut-sei=Zamami
en-aut-mei=Yoshito
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=9
ORCID=

en-aut-name=UeharaTakashi
en-aut-sei=Uehara
en-aut-mei=Takashi
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=10
ORCID=

affil-num=1
en-affil=Department of Medicinal Pharmacology, Graduate School of Medicine, Dentistry, and Pharmaceutical Sciences, Okayama University
kn-affil=

affil-num=2
en-affil=Department of Pharmacy, Okayama University Hospital
kn-affil=

affil-num=3
en-affil=Department of Clinical Pharmacy, Graduate School of Medicine, Dentistry, and Pharmaceutical Sciences, Okayama University
kn-affil=

affil-num=4
en-affil=Department of Clinical Pharmacy, Graduate School of Medicine, Dentistry, and Pharmaceutical Sciences, Okayama University
kn-affil=

affil-num=5
en-affil=Department of Pharmacy, Okayama University Hospital
kn-affil=

affil-num=6
en-affil=Department of Clinical Pharmacy Practice, School of Pharmacy & Pharmaceutical Sciences, University of California
kn-affil=

affil-num=7
en-affil=Division of Cardiology, School of Medicine, University of California
kn-affil=

affil-num=8
en-affil=Medical AI Project, Dentistry and Pharmaceutical Science, Okayama University Graduate School of Medicine
kn-affil=

affil-num=9
en-affil=Department of Pharmacy, Okayama University Hospital
kn-affil=

affil-num=10
en-affil=Department of Medicinal Pharmacology, Graduate School of Medicine, Dentistry, and Pharmaceutical Sciences, Okayama University
kn-affil=
en-keyword=machine learning
kn-keyword=machine learning
en-keyword=immune checkpoint inhibitor
kn-keyword=immune checkpoint inhibitor
en-keyword=myocarditis
kn-keyword=myocarditis
en-keyword=adverse event
kn-keyword=adverse event
END