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The more serious adverse outcomes (classes 3 and 4) occurred less frequently.
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One sort of medical waiting list can be cut by making another longer (leading to worse health outcomes); class sizes in schools can be reduced by mixing children of different ages and abilities together (leading to worse educational outcomes); and so on.
The Patient Outcomes class includes the concept of Social and/or Economic Impact to which our safety marker Out of pocket maps well.
Using the OOB data, actual outcome classes are cross-tabulated against forecasted outcome classes.
There can be similar reasoning for the outcome classes themselves.
As a consequence, the subspaces for both outcome classes are similar to each other.
Because there are three outcome classes, there are three such figures.
We can also see in Fig. 9 of SubVIS that the detected subspaces for both outcome classes are quite similar and there are no subspaces which are specific of one outcome class.
For instance, in the Kohonen network, the training phase usually starts by giving the initial weights which control the order of the outcome classes.
The acronym RIFLE stands for the increasing severity classes, risk (R) = 1, injury (I) = 2, and failure (F) = 3, and the two outcome classes, loss (L), and end-stage kidney disease (E).
We assume that the poor classification performance is caused by (1) the size of the training dataset which is too small, and (2) there are no global aspects allowing a classification into the two outcome classes.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com