Machine learning for clinical decision-making in cystic fibrosis care

New research shows machine learning could significantly augment clinical decision-making in cystic fibrosis care

Alan Turing Institute 

• New research published in Scientific Reports (see below), demonstrates that machine learning methods can predict with a 35% improvement in accuracy whether a cystic fibrosis (CF) patient should be referred for a lung transplant, in comparison to existing statistical methods. It is the first machine learning study to make use of a dataset representing 99% of CF patients living in the UK, the CF Registry.
• The research,has been generated through a partnership between The Alan Turing Institute and the Cystic Fibrosis Trust.

Reference: Prognostication and Risk Factors for Cystic Fibrosis via Automated Machine Learning Ahmed M. Alaa & Mihaela van der Schaar .
Scientific Reports vol8, Article number: 11242, 26 July 2018