Published on in Vol 10, No 5 (2022): May

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/26801, first published .
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

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

Journals

  1. Nurmambetova E, Pan J, Zhang Z, Wu G, Lee S, Southern D, Martin E, Ho C, Xu Y, Eastwood C. Developing an Inpatient Electronic Medical Record Phenotype for Hospital-Acquired Pressure Injuries: Case Study Using Natural Language Processing Models. JMIR AI 2023;2:e41264 View
  2. Birlik A, Tozan H, Köse K, Lu H. Machine learning-based hybrid risk estimation system (ERES) in cardiac surgery: Supplementary insights from the ASA score analysis. PLOS Digital Health 2025;4(6):e0000889 View
  3. Jin X, Wang Y, Wang J, Gao Q, Huang Y, Shao L, Zhao J, Li J, Li L, Zhang Z, Li S, Liu Y. A Machine Learning Approach to Differentiate Cold and Hot Syndrome in Viral Pneumonia Integrating Traditional Chinese Medicine and Modern Medicine: Machine Learning Model Development and Validation. JMIR Medical Informatics 2025;13:e64725 View
  4. Kim M, Kim Y, Kang H, Seo H, Choi H, Han J, Kee G, Ko S, Jung H, Kim B, Choi B, Jun T, Kim Y. Leveraging BERT for embedding ICD codes from large scale cardiovascular EMR data to understand patient diagnostic patterns. BMC Medical Informatics and Decision Making 2025;25(1) View

Books/Policy Documents

  1. Rodrigues J, Sikkander A, Tripathi S, Kumar K, Mishra S, Theivanathan G. Computational Intelligence for Genomics Data. View