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What if patients who are about to have a total joint replacement could have information about how, not everyone but, only people who are most like them did when they had their surgery?
We will discuss how we created an information tool that does just that using data from HUG patients. We will unpick how we applied conditional inference tree analysis to data from the Geneva Hip Registry to create classification algorithms based on pre-operative predictors to produce clusters of patients with homogeneous outcomes of pain, activity, complications, and the risk of having a contralateral hip replacement.
We will discuss how we created an information tool that does just that using data from HUG patients. We will unpick how we applied conditional inference tree analysis to data from the Geneva Hip Registry to create classification algorithms based on pre-operative predictors to produce clusters of patients with homogeneous outcomes of pain, activity, complications, and the risk of having a contralateral hip replacement.
Intervenants
Rafael Pinedo-Villanueva (PhD, Associate Professor, Senior Researcher in Health Economics Centre for Statistics in Medicine, NDORMS, University of Oxford)
Lieu
Bâtiment Prévost
Aile Salève (service de dermatologie)
Salle 7A-4-718
Possibilité de suivre le colloque via Teams:
ID de réunion : 393 184 435 332
Code secret : Qm2VTx
Présentation en anglais
Entrée
Gratuite
Organisateur(s)
Fichiers joints
Contact
malek.cicetti@hcuge.ch