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

BACKGROUND: Although 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. OBJECTIVE: We aimed to develop and valida...

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Detalles Bibliográficos
Autores principales: Kwon, Osung, Na, Wonjun, Kang, Heejun, Jun, Tae Joon, Kweon, Jihoon, Park, Gyung-Min, Cho, YongHyun, Hur, Cinyoung, Chae, Jungwoo, Kang, Do-Yoon, Lee, Pil Hyung, Ahn, Jung-Min, Park, Duk-Woo, Kang, Soo-Jin, Lee, Seung-Whan, Lee, Cheol Whan, Park, Seong-Wook, Park, Seung-Jung, Yang, Dong Hyun, Kim, Young-Hak
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2022
Materias:
Original Paper
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9133980/
https://www.ncbi.nlm.nih.gov/pubmed/35544292
http://dx.doi.org/10.2196/26801

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9133980/
https://www.ncbi.nlm.nih.gov/pubmed/35544292
http://dx.doi.org/10.2196/26801

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