Sentence examples for models for diagnosis from inspiring English sources

The proposed multi-sensor health diagnosis methodology using DBN based state classification can be structured in three consecutive stages: first, defining health states and preprocessing sensory data for DBN training and testing; second, developing DBN based classification models for diagnosis of predefined health states; third, validating DBN classification models with testing sensory dataset.

Using multivariate statistical analysis we could generate powerful models for diagnosis and prognosis of septic shock.

Logistic regression and ANN are currently the most widely used models for diagnosis and prognosis studies in biomedicine.

Different prediction models for diagnosis of (persistent) erosive disease have been described by means of different ACPA assays [ 22, 23].

PAH severity at baseline (study enrollment) was also accounted for after adjusting the models for diagnosis status, PAH medications, and risk profile at baseline.

Prediction models for diagnosis using either criterion were similar, with major baseline variables including glycemia by either measure, obesity, WAIST, and UACR.

We designed a telemedicine model for diagnosis of common, acute illness to compare telemedicine and in-person evaluations on reproducibility of diagnosis and treatment.

Objective: We sought to test a 3-parameter model for diagnosis of dizziness based on the type and temporal characteristics of the dizziness and on hearing status.

We test our method on Hepar II, a model for diagnosis of liver disorders, whose parameters are extracted from a real, small set of patient records.

The objective of this study was to characterize patient demographics, risk factors, and anatomic distribution of upper extremity deep venous thrombosis (UEDVT) to develop a probability model for diagnosis.

This work proposes a KE methodology called Timed Observation Modelling For Diagnosis (TOM4D) which allows building dynamic process models from experts' knowledge and data where the obtained models can be compared and combined with models obtained through a KDD process in order to define a model more suitable to the dynamic process reality.