Can Machine Learning Algorithms Predict Which Patients Will Achieve Minimally Clinically Important Differences From Total Joint Arthroplasty?

Can Machine Learning Algorithms Predict Which Patients Will Achieve Minimally Clinically Important Differences From Total Joint Arthroplasty?
Clin Orthop Relat Res. 2019 Jun;477(6):1267-1279.

• The three machine learning algorithms were tested using hospital registry data regarding total joint arthroplasty (TJA) patients.They were found to have the potential to improve clinical decision-making and patient care by helping to prioritize resources for postsurgical monitoring and informing presurgical discussions of likely outcomes of TJA.

See also

• Artificial Intelligence May Lead to Better Outcomes After Joint Surger, PRNewswire 16 May 2019

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