For improved shared decision making, formal prediction of patient-centered outcomes is necessary.
The formal prediction of the UCIP model for OR processing can be stated in terms of the integrated hazard function, H t), which indicates the amount of processing completed up to a given time (t).
Yet, the majority of hospitals (or treating physicians) do not use any of the formal prediction rules.
The main reasons for exclusion were no prediction of the future risk of diabetes (n=15); validation study (n=10); no formal prediction models provided (n=6); and incomparable derivation populations (n=2) or unavailable data of predictors (n=3).
Studies were included if they met the following criteria: the study presented at least one formal prediction model or an update on a previously developed model; the endpoint was incident type 2 diabetes in a longitudinal design; and the population had to be at least partly white because the EPIC-NL cohort to be used for validation consists predominantly of white adults.
Moreover, if they choose to opt out and hence to receive care as usual, this care as usual will be based on the clinician's professional judgment, which could be guided by the HEART score, by an alternative risk score that the physician used to apply previously, or by his or her overall risk assessment without formal prediction rules.
For exaggerated response prediction, formal multifactor prediction models have not been published, as most of the attention has focused on single-test predictors, such as AMH and AFC (Broer et al., 2011).
The gap between applying formal performance predictions and actually improving the design of a system can thus be closed.
Formal performance prediction methods, based on queueing network models, allow evaluating software architectural designs for performance.
In this paper, we present a formal performance prediction approach for component-based systems, which is parameterisable for the number of CPUs or CPU cores.
