Sentence examples for morbidity probability from inspiring English sources

Popular predictive models for estimating morbidity probability after heart surgery are compared critically in a unitary framework.

In the first part different methods for estimating morbidity probability were grouped into categories according to the underlying mathematical principles.

In other words, in the case of a predictive model of morbidity probability with good discrimination and poor calibration, it is possible to improve calibration without modifying discrimination capacity [ 57].

Once optimized to ensure suitable generalization with the best discrimination performance, models with inadequate calibration were recalibrated by applying a cubic monotonic transformation (see Part I of the study) to the ranked predicted probabilities, so as to reach a more reliable estimation of morbidity probability.

The following models were developed locally to predict morbidity probability: Bayesian linear (BL) model, Bayesian quadratic (BQ) model, k-nearest neighbour (kNN) model, logistic regression (LR) model, Higgins score (HS) model derived from the previous LR model, direct score (DS) model, and two feed-forward artificial neural networks (ANNs) with one and two layers (ANN1 and ANN2, respectively).

Patients with scores of 0, 1, 2, 3 and ≥5 had clearly separated confidence intervals for morbidity probabilities.

P concisely indicates the morbidity risk probability, that is P(M | x ), and P rec is the corresponding recalibrated value.

Large rotator cuff tears, which include more than one of the rotator cuff tendons, lead to increased morbidity and probability of post-surgical repair-site failure [ 40].

In this first part we describe a variety of models to predict morbidity risk probability in the ICU and discuss their theoretical advantages and disadvantages.

We need to remember that although mortality is a critical outcome, it may not closely reflect other important dimensions of quality such as patient satisfaction, morbidity, or probability of readmissions.

All critically ill patients admitted to an ICU in the CHR during the study period were included in the sample, therefore reducing susceptibility to selection bias from referral, but not necessarily excluding bias in cases where critically ill patients were refused admission to ICU based on underlying co-morbidities and probability for death.