Development and training of a machine learning algorithm to identify patients at risk for recurrence following an arthroscopic Bankart repair (CLEARER): protocol for a retrospective, multicentre, cohort study.

Sanne H van SpanningLukas P E VerweijLaurens J H AllaartLaurent A M HendrickxJob N DoornbergGeorge S AthwalThibault LafosseLaurent LafosseMichel P J van den BekeromGeert Alexander Buijzenull null

Published in: BMJ open (2022)

For safe multicentre data exchange and analysis, our Machine Learning Consortium adhered to the WHO regulation 'Policy on Use and Sharing of Data Collected by WHO in Member States Outside the Context of Public Health Emergencies'. The study results will be disseminated through publication in a peer-reviewed journal. No Institutional Review Board is required for this study.

Keyphrases

machine learning
public health
big data
end stage renal disease
randomized controlled trial
artificial intelligence
electronic health record
study protocol
ejection fraction
newly diagnosed
deep learning
chronic kidney disease
social media
peritoneal dialysis
health information
data analysis
virtual reality