Sentence examples for models for morbidity from inspiring English sources
In a study comparing prognostic models for morbidity prediction after cardiopulmonary bypass surgery in an Italian hospital, Biagoli and coworkers [ 1] found a Bayesian linear model to have greater discriminatory power compared with simple scoring models, even when customized to a local population.
The aim of this project was to identify different perinatal risk indicators and to develop prediction models for neurologic morbidity at 2 and 5 years of age.
Bayes linear classifiers and score models for predicting morbidity risk in intensive care patients after coronary artery surgery are compared.
Popular predictive models for estimating morbidity probability after heart surgery are compared critically in a unitary framework.
Different authors tend to use their own criteria to compare the performances of different risk models for predicting morbidity [ 23- 25].
Adjusted models for all morbidity outcomes (infectious disease and malnutrition indicators), which were based on current health indicators of living children, included age of the child, sex of the child, whether the child was from a multiparous birth, area of residence (urban v rural), national region, religion, maternal age at interview, maternal education, and maternal BMI.
Different approaches for developing predictive models of morbidity in cardiac postoperative intensive care units have been reviewed in a unitary framework from a theoretical point of view.
The clinical model accounted for morbidities in three ways: outpatient conditions inferred from data on dispensed drugs; inpatient conditions only; both conditions.
In the present study, we developed and tested a Bayesian discrimination model for assessing morbidity risk after coronary artery surgery.
Of course, this does not mean that the Bayes quadratic approach is always the best model for predicting morbidity in ICU patients.
Higgins and colleagues proposed a procedure for designing a simple-to-use score model (HS model) for predicting morbidity risk on admission to ICU after coronary artery bypass grafting [ 7].
