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10.1111/dme.70186 http://scihub22266oqcxt.onion/10.1111/dme.70186 41351387!?!41351387       Diabet+Med 2025 ; ? (?): e70186 Nephropedia Template TP {{tp|p=41351387|t=2025. Early, unplanned readmission in diabetes inpatients: Comparing the utility and performance of machine learning and traditional prediction models to guide timely diabetes management team review |pdf=|usr=}}{{41351387}} gab.com Text Early, unplanned readmission in diabetes inpatients: Comparing the utility and performance of machine learning and traditional prediction models to guide timely diabetes management team review JO: Diabet Med http://www.kidney.de/pget.php?&tw=1&pmid=41351387 #FOAvip BACKGROUND: Inpatients with diabetes have higher early unplanned readmission (EUR) rates. Diabetes management team (DMT) review reduces EUR. While glucose-based patient selection for DMT reduces in-hospital adverse outcomes, this single criterion is a suboptimal predictor of EUR. AIM: We developed, compared and externally validated four EUR prediction models in diabetes inpatients using primarily early admission data to facilitate timely review. Secondarily, we investigated how combining predictive and glucose data can refine patient selection for DMT review. METHODS: We constructed three traditional models (classification tree, logistic group lasso, elastic net) and a neural network model using 14 routinely available variables. Models were externally validated and performance assessed by area under the curve (AUC). We analysed the overlap between high-risk patients and those with abnormal glucose (>/=1 glucose level <4 or >15 mmol/L) according to pre-specified sensitivities (25%, 50%, 75%). RESULTS: Group lasso, elastic net and neural network performed similarly (AUC 0.722-727 test cohort, 0.653-0.667 validation), outperforming the tree (AUC 0.663 test cohort, 0.595 validation). These models identified 9%, 21%-23% and 41%-42% of admissions as 'high risk' using respective sensitivities of 25%, 50% and 75%. In the group lasso, approximately half of 'high-risk' patients also had abnormal glucose which reduced the DMT review cohort to 4.9%, 10.8% and 19.2% for sensitivities of 25%, 50% and 75%. CONCLUSION: EUR prediction models facilitate targeted, timely DMT review. Regularised regression models offer a feasible, practical approach for identifying high-risk patients in resource-limited hospital settings. Combining model-identified risk with abnormal glucose refines patient selection, optimising resource allocation. Twit Text FOAVip Early, unplanned readmission in diabetes inpatients: Comparing the utility and performance of machine learning and traditional prediction models to guide timely diabetes management team review ????Diabet Med http://www.kidney.de/pget.php?&tw=1&pmid=41351387 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=41351387 #FOAvip English Wikipedia <ref>{{Cite pmid|41351387}}</ref> Early, unplanned readmission in diabetes inpatients: Comparing the utility and performance of machine learning and traditional prediction models to guide timely diabetes management team review #MMPMID41351387 Lau SM; Popovic G; Maslen B; Depczynski B Diabet Med 2025[Dec]; ? (?): e70186 PMID41351387show ga BACKGROUND: Inpatients with diabetes have higher early unplanned readmission (EUR) rates. Diabetes management team (DMT) review reduces EUR. While glucose-based patient selection for DMT reduces in-hospital adverse outcomes, this single criterion is a suboptimal predictor of EUR. AIM: We developed, compared and externally validated four EUR prediction models in diabetes inpatients using primarily early admission data to facilitate timely review. Secondarily, we investigated how combining predictive and glucose data can refine patient selection for DMT review. METHODS: We constructed three traditional models (classification tree, logistic group lasso, elastic net) and a neural network model using 14 routinely available variables. Models were externally validated and performance assessed by area under the curve (AUC). We analysed the overlap between high-risk patients and those with abnormal glucose (>/=1 glucose level <4 or >15 mmol/L) according to pre-specified sensitivities (25%, 50%, 75%). RESULTS: Group lasso, elastic net and neural network performed similarly (AUC 0.722-727 test cohort, 0.653-0.667 validation), outperforming the tree (AUC 0.663 test cohort, 0.595 validation). These models identified 9%, 21%-23% and 41%-42% of admissions as 'high risk' using respective sensitivities of 25%, 50% and 75%. In the group lasso, approximately half of 'high-risk' patients also had abnormal glucose which reduced the DMT review cohort to 4.9%, 10.8% and 19.2% for sensitivities of 25%, 50% and 75%. CONCLUSION: EUR prediction models facilitate targeted, timely DMT review. Regularised regression models offer a feasible, practical approach for identifying high-risk patients in resource-limited hospital settings. Combining model-identified risk with abnormal glucose refines patient selection, optimising resource allocation. ?Early, unplanned readmission in diabetes inpatients: Comparing the uti(epmc).pdf DeepDyve Pubget Overpricing