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However, the use of such algorithms can be a hard task for the software engineer, mainly due to the significant range of parameter and algorithm choices.
We suggest a more frequent and wider use of such algorithms in warranty policies.
This limitation hinders the use of such algorithms, for example, in a relational database which consists of several tables semantically related [2].
From the health system perspective, use of such algorithms would necessitate the evaluation of several patients without knee or hip OA for each one definitively diagnosed with these conditions, something usual on screening tools.
It has also been shown that reproducibility of results from the use of such algorithms can drastically decrease, yielding low inter-rater variability, because clinical judgment is always necessary to be able to answer all the questions included [ 61].
However, use of such algorithms may exclude less severe cases that do not require treatment, and the inclusion of test results in these algorithms is problematic since not all test results are recorded in the GPRD.
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Reduction of time spent in hypoglycemia is important in view of future home use of such algorithm-driven insulin-infusion systems.
Using of such algorithms for patients who sustained multiple brain injuries was proposed by Maas et. al. [ 6].
Here, we have used one of such algorithms, the Incremental Association Markov Blanket (IAMB) algorithm [ 25].
This article explains the theoretical concepts of the search algorithms used for finding such representations, discusses various types of such algorithms, and exemplifies their use in the context of obesity and insulin resistance.
The use, and most importantly, the success of such algorithms will depend critically on the availability of data from all possible damage states for training.
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