Electronic Medical Record–Based Machine Learning Approach to Predict the Risk of 30-Day Adverse Cardiac Events After Invasive Coronary Treatment: Machine Learning Model Development and Validation | Zendy

Osung Kwon | Zendy; Wonjun Na | Zendy; Hee Jun Kang | Zendy; Tae Joon Jun | Zendy; Jihoon Kweon | Zendy; GyungMin Park | Zendy; YongHyun Cho | Zendy; Cinyoung Hur | Zendy; Jungwoo Chae | Zendy; DoYoon Kang | Zendy; Pil Hyung Lee | Zendy; JungMin Ahn | Zendy; DukWoo Park | Zendy; SooJin Kang | Zendy; SeungWhan Lee | Zendy; Cheol Whan Lee | Zendy; SeongWook Park | Zendy; SeungJung Park | Zendy; Dong Hyun Yang | Zendy; YoungHak Kim | Zendy

Open Access

Electronic Medical Record–Based Machine Learning Approach to Predict the Risk of 30-Day Adverse Cardiac Events After Invasive Coronary Treatment: Machine Learning Model Development and Validation

Author(s) -

Osung Kwon,

Wonjun Na,

Hee Jun Kang,

Tae Joon Jun,

Jihoon Kweon,

GyungMin Park,

YongHyun Cho,

Cinyoung Hur,

Jungwoo Chae,

DoYoon Kang,

Pil Hyung Lee,

JungMin Ahn,

DukWoo Park,

SooJin Kang,

SeungWhan Lee,

Cheol Whan Lee,

SeongWook Park,

SeungJung Park,

Dong Hyun Yang,

YoungHak Kim

Publication year - 2022

Publication title -

jmir medical informatics

Language(s) - English

Resource type - Journals

ISSN - 2291-9694

DOI - 10.2196/26801

Subject(s) - medicine , receiver operating characteristic , brier score , random forest , gradient boosting , machine learning , logistic regression , percutaneous coronary intervention , adverse effect , artificial intelligence , medical record , emergency medicine , computer science , myocardial infarction

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