AI Algorithm to Predict Acute Coronary Syndrome in Prehospital Cardiac Care: Retrospective Cohort Study.
Enrico R de KoningYvette van der HaasSaguna SagunaEsmee StoopJan BoschSaskia Lambertha Maria Anna BeeresMartin Jan SchalijMark J BoogersPublished in: JMIR cardio (2023)
The AI model was able to predict ACS based on retrospective data from the prehospital setting. It led to an increase in specificity (from 1% to 11%) and NPV (from 94% to 99%) when compared to usual care, with a similar sensitivity. Due to the retrospective nature of this study and the singular focus on ACS it should be seen as a proof-of-concept. Other (possibly life-threatening) diagnoses were not analyzed. Future prospective validation is necessary before implementation.
Keyphrases
- acute coronary syndrome
- healthcare
- quality improvement
- cardiac arrest
- percutaneous coronary intervention
- antiplatelet therapy
- palliative care
- artificial intelligence
- cross sectional
- machine learning
- primary care
- trauma patients
- deep learning
- emergency medical
- affordable care act
- left ventricular
- electronic health record
- coronary artery disease
- neural network
- clinical evaluation