Artificial intelligence assisted risk prediction in organ transplantation: a UK Live-Donor Kidney Transplant Outcome Prediction tool
Hatem Kaies Ibrahim Elsayed Ali(University Hospitals Coventry and Warwickshire NHS Trust), Nithya Krishnan(University Hospital Coventry), Miklos Z. Molnar(University of Tennessee Health Science Center), B. F. Burke(Massachusetts Institute of Technology), Tibor Fülöp(Medical University of South Carolina), David Briggs(Imperial College London), Sunil Shroff, Arun Shroff(Flinders Medical Centre), Adnan Sharif(Queen Elizabeth Hospital Birmingham)
Cited by 13
Related Papers
Improving health through policies that promote active travel: A review of evidence to support integrated health impact assessment
|Environment International|2011|620
Proceedings From an International Consensus Meeting on Posttransplantation Diabetes Mellitus: Recommendations and Future Directions
|American Journal of Transplantation|2014|522
Development of a cross-platform biomarker signature to detect renal transplant tolerance in humans
|Journal of Clinical Investigation|2010|516
Acute Kidney Injury After Major Surgery: A Retrospective Analysis of Veterans Health Administration Data
|American Journal of Kidney Diseases|2015|364
Comparison of organ donation and transplantation rates between opt-out and opt-in systems
|Kidney International|2019|282