Exact(5)
Specifically trends in fire incidence are compared with both census-derived data and small area geodemographic classifications in order to assess the value of such classifications as exploratory tools in investigating potential associations with socio-economic patterns.
Although classification of patients is possible into broadly defined clinical and pathological groups, defining the borders of such classifications is often hampered by individual variation between patients, even within families with Mendelian forms of disease [e.g. as is the case in Frontotemporal dementia (FTD), [1] [3]].
Some of these studies could address the utility of such classifications for guiding clinicians regarding different interventions of AKI therapy.
Econometric modeling has already demonstrated the sensitivity of such classifications to differences, and changes, in available patterns of health and social services in specific milieu [ 40, 73].
Although classification of AF into different types is clinically useful to select for the appropriate treatment, the shortcomings of such classifications becomes obvious when applied at a single point in time such as at study entry.
Similar(55)
We provide a comprehensive review of such classification methods, including ensemble classification, subcategorization, and sparse representation.
On top of such classification, we have built an iterative attacking algorithm.
Performance of such classification models mostly depends on features used to represent samples in a training dataset.
By means of the Kranoselskii's fixed point theorem, existence criteria are then provided for justification of such classification.
Analysis of principies to which classification should match is presented and functions of such classification such as definitive, characteristic and predictive are discussed.
OMENS system is one of such classification, which represents the abbreviation of first letter of orbit, mandible, ear, nerve, and soft tissue [9].
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