TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods.

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Title: TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods.
Authors: Collins GS; Centre for Statistics in Medicine, UK EQUATOR Centre, Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK., Moons KGM; Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands., Dhiman P; Centre for Statistics in Medicine, UK EQUATOR Centre, Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK., Riley RD; Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK.; National Institute for Health and Care Research (NIHR) Birmingham Biomedical Research Centre, Birmingham, UK., Beam AL; Department of Epidemiology, Harvard T H Chan School of Public Health, Boston, MA, USA., Van Calster B; Department of Development and Regeneration, KU Leuven, Leuven, Belgium.; Department of Biomedical Data Science, Leiden University Medical Centre, Leiden, Netherlands., Ghassemi M; Department of Electrical Engineering and Computer Science, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA., Liu X; Institute of Inflammation and Ageing, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK.; University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK., Reitsma JB; Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands., van Smeden M; Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands., Boulesteix AL; Institute for Medical Information Processing, Biometry and Epidemiology, Faculty of Medicine, Ludwig-Maximilians-University of Munich and Munich Centre of Machine Learning, Germany., Camaradou JC; Patient representative, Health Data Research UK patient and public involvement and engagement group.; Patient representative, University of East Anglia, Faculty of Health Sciences, Norwich Research Park, Norwich, UK., Celi LA; Beth Israel Deaconess Medical Center, Boston, MA, USA.; Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA, USA.; Department of Biostatistics, Harvard T H Chan School of Public Health, Boston, MA, USA., Denaxas S; Institute of Health Informatics, University College London, London, UK.; British Heart Foundation Data Science Centre, London, UK., Denniston AK; National Institute for Health and Care Research (NIHR) Birmingham Biomedical Research Centre, Birmingham, UK.; Institute of Inflammation and Ageing, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK., Glocker B; Department of Computing, Imperial College London, London, UK., Golub RM; Northwestern University Feinberg School of Medicine, Chicago, IL, USA., Harvey H; Hardian Health, Haywards Heath, UK., Heinze G; Section for Clinical Biometrics, Centre for Medical Data Science, Medical University of Vienna, Vienna, Austria., Hoffman MM; Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada.; Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada.; Department of Computer Science, University of Toronto, Toronto, ON, Canada.; Vector Institute for Artificial Intelligence, Toronto, ON, Canada., Kengne AP; Department of Medicine, University of Cape Town, Cape Town, South Africa., Lam E; Patient representative, Health Data Research UK patient and public involvement and engagement group., Lee N; National Institute for Health and Care Excellence, London, UK., Loder EW; The BMJ, London, UK.; Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA., Maier-Hein L; Department of Intelligent Medical Systems, German Cancer Research Centre, Heidelberg, Germany., Mateen BA; Institute of Health Informatics, University College London, London, UK.; Wellcome Trust, London, UK.; Alan Turing Institute, London, UK., McCradden MD; Department of Bioethics, Hospital for Sick Children Toronto, ON, Canada.; Genetics and Genome Biology, SickKids Research Institute, Toronto, ON, Canada., Oakden-Rayner L; Australian Institute for Machine Learning, University of Adelaide, Adelaide, SA, Australia., Ordish J; Medicines and Healthcare products Regulatory Agency, London, UK., Parnell R; Patient representative, Health Data Research UK patient and public involvement and engagement group., Rose S; Department of Health Policy and Center for Health Policy, Stanford University, Stanford, CA, USA., Singh K; Department of Epidemiology, CAPHRI Care and Public Health Research Institute, Maastricht University, Maastricht, Netherlands., Wynants L; Department of Epidemiology, CAPHRI Care and Public Health Research Institute, Maastricht University, Maastricht, Netherlands., Logullo P; Centre for Statistics in Medicine, UK EQUATOR Centre, Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK.
Source: BMJ (Clinical research ed.) [BMJ] 2024 Apr 16; Vol. 385, pp. e078378. Date of Electronic Publication: 2024 Apr 16.
Publication Type: Journal Article
Journal Info: Publisher: British Medical Association Country of Publication: England NLM ID: 8900488 Publication Model: Electronic Cited Medium: Internet ISSN: 1756-1833 (Electronic) Linking ISSN: 09598138 NLM ISO Abbreviation: BMJ Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1756-1833
DOI:10.1136/bmj-2023-078378