An experiment to identify if AI models can predict if a person will go into cardiac arrest.
Cardiac arrest is a major burden on the healthcare industry, with approximately 292,000 in-hospital cardiac events reported in the United States annually (Holmberg et al., 2019). Identifying cardiac events before they occur can provide healthcare systems with critical lead time, allowing for more efficient resource allocation for high-risk patients. Recent research (Kwon et al., 2020) has demonstrated that deep learning algorithms can effectively predict cardiac arrest within 24 hours using standard 12-lead ECGs, and even single-lead ECGs.
Holmberg, M. J., Ross, C. E., Fitzmaurice, G. M., Chan, P. S., Duval-Arnould, J., Grossestreuer, A. V., Yankama, T., Donnino, M. W., Andersen, L. W., Chan, P., Grossestreuer, A. V., Moskowitz, A., Edelson, D., Ornato, J., Berg, K., Peberdy, M. A., Churpek, M., Kurz, M., Starks, M. A., & Girotra, S. (2019). Annual Incidence of Adult and Pediatric In-Hospital Cardiac Arrest in the United States. Circulation: Cardiovascular Quality and Outcomes, 12(7). https://doi.org/10.1161/circoutcomes.119.005580
Kwon, J., Kim, K.-H., Jeon, K.-H., Lee, S. Y., Park, J., & Oh, B.-H. (2020). Artificial intelligence algorithm for predicting cardiac arrest using electrocardiography. Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine, 28(1). https://doi.org/10.1186/s13049-020-00791-0