L E Nield, C Manlhiot, K Magor, L Freud, B Chinni, A Ims, N Melamed, O Nevo, T Van Mieghem, D Weisz, S Ronzoni
Prediction of outcomes following a prenatal diagnosis of congenital heart disease (CHD) is challenging. Machine learning (ML) algorithms may be used to reduce clinical uncertainty and improve prognostic accuracy. We performed a pilot study to train ML algorithms to predict postnatal outcomes based on clinical data. Specific objectives were to predict (1) in utero or neonatal death, (2) high-acuity neonatal care and (3) favorable outcomes. We included all fetuses with cardiac disease at Sunnybrook Health Sciences Centre, Toronto, Canada, from 2012 to 2021...
May 9, 2024: Pediatric Cardiology