https://ogma.newcastle.edu.au/vital/access/ /manager/Index ${session.getAttribute("locale")} 5 https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:25021 0.6. Shrinkage was required for all predictive models' coefficients ranging from -0.309 (prediction probability was inverse to observed proportion) to 0.823. Predictive models which include baseline symptoms as a feature produced the highest discrimination. Two models produced a predicted probability of 0 and 1 for all patients. Conclusions: Predictive models vary in performance and transferability illustrating the need for improvements in model development and reporting. Several models showed reasonable potential but efforts should be increased to improve performance. Baseline symptoms should always be considered as potential features for predictive models.]]> Wed 19 Jan 2022 15:16:35 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:16753 Wed 11 Apr 2018 15:29:36 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:34316 Thu 03 Feb 2022 12:21:50 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:22558 Sat 24 Mar 2018 07:14:46 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:24849 0.6 while all haematuria endpoints and longitudinal incontinence models produced AUROC<0.6. Conclusions: Logistic regression and MARS were most likely to be the best-performing strategy for the prediction of urinary symptoms with elastic-net and random forest producing competitive results. The predictive power of the models was modest and endpoint-dependent. New features, including spatial dose maps, may be necessary to achieve better models.]]> Sat 24 Mar 2018 07:11:24 AEDT ]]>