Sentence examples for model that categorizes from inspiring English sources

Thus, an alternative model that categorizes hospitalization for evaluation separately may increase performance in Japanese cases and deserves further consideration in future studies.

Choueiri et al (2007) have reported that five clinical factors, – time-to-diagnosis, baseline calcium and platelet and neutrophil counts and performance status are independent prognostic factors for patients with MRCC treated with anti-VEGF therapy and have developed a prognostic model that categorizes such patients into three prognostic groups.

Thus, we applied a scaled-down version of the model that categorized patients as either slow or fast BCR-ABL1 responders.

Machine-learning algorithms (Hall et al., 2009b) and training sets of known membrane-localized and non-membrane-localized proteins were used to build a model that categorized each protein as belonging to the cell surface or not.

Flagging clickbait headlines or filtering spam both require an underlying model that can interpret and categorize language.

What he did, first as a graduate student at Princeton and later at the testing service, was devise a trailblazing mathematical model that enabled test writers to categorize particular questions, based on their difficulty.

"Through as few as 50 examples, Comprehend will automatically train a custom classification model that can be used to categorize all your documents.

The mean blood pressure values were compared between races and across time using a general linear model that adjusted for age, sex, region (categorized as stroke belt, stroke buckle, rest of the nation), exercise, income, education, smoking, total and HDL cholesterol, and the number of antihypertensive medications.

In this paper, we propose a taxonomy that categorizes the existing simulation tools into four groups, i.e., network simulators with node models, network simulators with node emulators, node system simulator with network models, and node emulators with network models.

Many researchers have studied the infiltration problem and have developed models that can be categorized by analytical and numerical concepts.

We addressed the possible effects of selection bias due to exclusion of patients with missing viral load outcomes by modeling a worst-case scenario that categorized all missing virologic outcomes as detectable (>400 copies/mL) viral loads.