https://nova.newcastle.edu.au/vital/access/ /manager/Index ${session.getAttribute("locale")} 5 https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:5022 Wed 22 Mar 2023 16:11:49 AEDT ]]> https://nova.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://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:16753 Wed 11 Apr 2018 15:29:36 AEST ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:26791 Sat 24 Mar 2018 07:36:30 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:22835 1 timepoints. Associations at a single timepoint were found for cerebrovascular condition, ECOG status and non-steroidal anti-inflammatory drug intake. Peak incidence analysis shows the impact of baseline, bowel and cerebrovascular condition and smoking status. Conclusions: The prevalence and incidence analysis provide a complementary view for urinary symptom prediction. Sustained impacts across time points were found for several factors while some associations were not repeated at different time points suggesting poorer or transient impact.]]> Sat 24 Mar 2018 07:16:08 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:22558 Sat 24 Mar 2018 07:14:46 AEDT ]]> https://nova.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 ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:42619 Mon 04 Sep 2023 13:58:46 AEST ]]>