Machine Learning in Paediatric Cardiac Surgery: Ready for Prime Time? - 13/04/22
Doi : 10.1016/j.hlc.2021.12.005
Kim S. Betts, PhD, MPH a, Supreet P. Marathe, MD, MCh b, c, d, Jessica Suna, MHlthSci b, c, d, Prem Venugopal, FRACS b, c, d, Kevin Chai, PhD a, Nelson Alphonso, FRACS b, c, d, ⁎ 
Queensland Paediatric Cardiac Research (QPCR) Group
Janelle Johnson, Kathryn Versluis, Tom R. Karl
a School of Population Health, Curtin University, Perth, WA, Australia
b Queensland Paediatric Cardiac Service (QPCS), Queensland Children’s Hospital, Brisbane, Qld, Australia
c Children’s Health Research Centre, University of Queensland, Brisbane, Qld, Australia
d School of Clinical Medicine, Children’s Health Queensland Clinical Unit, University of Queensland, Brisbane, Qld, Australia
∗Corresponding author at: Queensland Children’s Hospital, Clinical Directorate 7f, Level 7, South Brisbane, Qld 4101, AustraliaQueensland Children’s HospitalClinical Directorate 7fLevel 7South BrisbaneQld4101Australia
Keywords : Machine learning, Risk prediction, Outcome analysis, Personalised medicine
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© 2021
Australian and New Zealand Society of Cardiac and Thoracic Surgeons (ANZSCTS) and the Cardiac Society of Australia and New Zealand (CSANZ). Tous droits réservés.
Vol 31 - N° 5
P. 613-615 - mai 2022 Retour au numéro
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