Cheryl Regehr, Arija Birze
Assessing the equivalency of face-to-face and online simulated patient interviews in an educational intervention
Advances in simulation (London, England)
despite significant progress last decades survival kidney allografts several risk factors remain contributing worsening kidney function even loss transplants aimed evaluate new machine learning method identify variables may predict early graft loss kidney transplant patients assess usefulness improving clinical decisions retrospective cohort study carried 627 kidney transplant patients followed least three months data preprocessed selected features used develop automatically working machine learning algorithm algorithm applied training parameterization model finally tested model used analysis patients features impactful prediction clinical outcomes models evaluated using area curve auc shapley additive explanations shap algorithm used interpret predictions final selected model achieved precision 081 sensitivity 061 specificity 089 auc value 084 model serum creatinine levels kidney transplant patients evaluated hospital discharge proved important factor decisionmaking allograft loss patients weight equivalent bmi closer normal range prior kidney transplant less likely experience graft loss compared patients bmi normal range age patients transplantation polyomavirus bkpyv infection significant impact clinical outcomes model algorithm suggests main characteristics impacted early allograft loss serum creatinine levels hospital discharge well pretransplant values body weight age patients bkpyv infection propose machine learning tools developed effectively assist medical decisionmaking kidney transplantation