Minimal Patient Clinical Variables to Accurately Predict Stress Echocardiography Outcome: Validation Study Using Machine Learning Techniques.
Mohamed BennasarDuncan BanksBlaine A PriceAttila KardosPublished in: JMIR cardio (2020)
This study shows that machine learning can predict the outcome of stress echocardiography based on only a few features: patient prior cardiac history, gender, and prescribed medication. Further research recruiting higher number of patients who underwent stress echocardiography could further improve the performance of the proposed algorithm with the potential of facilitating patient selection for early treatment/intervention avoiding unnecessary downstream testing.
Keyphrases
- left ventricular
- machine learning
- case report
- pulmonary hypertension
- computed tomography
- end stage renal disease
- randomized controlled trial
- healthcare
- chronic kidney disease
- ejection fraction
- newly diagnosed
- stress induced
- heart failure
- mental health
- artificial intelligence
- deep learning
- emergency department
- patient reported outcomes
- heat stress
- smoking cessation