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](https://asset.jmir.pub/assets/ff7f5f9713a768b52caecc9b32224077.png 480w,https://asset.jmir.pub/assets/ff7f5f9713a768b52caecc9b32224077.png 960w,https://asset.jmir.pub/assets/ff7f5f9713a768b52caecc9b32224077.png 1920w,https://asset.jmir.pub/assets/ff7f5f9713a768b52caecc9b32224077.png 2500w)
Journals
- 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