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

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

BackgroundAlthough there is a growing interest in prediction models based on electronic medical records (EMRs) to identify patients at risk of adverse cardiac events following invasive coronary treatment, robust models fully utilizing EMR data are limited. Objecti...

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Bibliographic Details
Main Authors: Osung Kwon, Wonjun Na, Heejun Kang, Tae Joon Jun, Jihoon Kweon, Gyung-Min Park, YongHyun Cho, Cinyoung Hur, Jungwoo Chae, Do-Yoon Kang, Pil Hyung Lee, Jung-Min Ahn, Duk-Woo Park, Soo-Jin Kang, Seung-Whan Lee, Cheol Whan Lee, Seong-Wook Park, Seung-Jung Park, Dong Hyun Yang, Young-Hak Kim
Format: Article
Language:English
Published: JMIR Publications 2022-05-01
Series:JMIR Medical Informatics
Online Access:https://medinform.jmir.org/2022/5/e26801

Internet

https://medinform.jmir.org/2022/5/e26801

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