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Related Experiment Video

Updated: Sep 23, 2025

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Electronic Medical Record-Based Machine Learning Approach to Predict the Risk of 30-Day Adverse Cardiac Events After

Osung Kwon1, Wonjun Na2, Heejun Kang3

  • 1Division of Cardiology Department of Internal Medicine, Eunpyeong St Mary's Hospital, Catholic University of Korea, Seoul, Republic of Korea.

JMIR Medical Informatics
|May 11, 2022
PubMed
Summary
This summary is machine-generated.

Machine learning models using electronic medical records (EMRs) can predict 30-day adverse cardiac events after invasive treatment. Gradient Boosting Machine (GBM) demonstrated superior performance, highlighting the potential of diverse EMR data for risk prediction.

Keywords:
adverse cardiac eventbig datacoronary artery diseaseelectronic medical recordmachine learningmortalityprediction

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Area of Science:

  • Cardiology
  • Medical Informatics
  • Machine Learning

Background:

  • Limited robust models exist for predicting adverse cardiac events post-invasive treatment using electronic medical records (EMRs).
  • Growing interest in leveraging EMR data for risk stratification in cardiovascular care.

Purpose of the Study:

  • To develop and validate machine learning (ML) models for predicting 30-day adverse cardiac events after percutaneous coronary intervention or bypass surgery.
  • To utilize diverse EMR fields, including static, time-series, and cardiac-specific data.

Main Methods:

  • Utilized a large EMR dataset (5,184,565 records, 16,793 patients) from 2006-2016.
  • Applied Logistic Regression (LR), Random Forest (RF), Gradient Boosting Machine (GBM), and Feedforward Neural Network (FNN) algorithms.
  • Validated models using 5-fold cross-validation and an external cohort, with 30-day mortality as the primary outcome.

Main Results:

  • GBM achieved the highest performance (AUROC 0.99, AUPRC 0.80) in internal validation; RF also performed well (AUROC 0.98).
  • External validation showed GBM with maximal performance (AUROC 0.90).
  • Time-series dynamic EMR data significantly contributed to model performance (AUROC >0.95).

Conclusions:

  • ML models effectively predict 30-day adverse cardiac events using diverse EMR data.
  • The Gradient Boosting Machine model demonstrated superior predictive accuracy.
  • The developed framework is generalizable for various healthcare prediction applications.