  The dose regimen of Warfarin is separated into two phases. Firstly a loading
dose is given, which is designed to bring the International Normalisation Ratio
(INR) to within therapeutic range. Then a stable maintenance dose is given to
maintain the INR within therapeutic range. In the United Kingdom (UK) the
loading dose is usually given as three individual daily doses, the standard
loading dose being 10mg on days one and two and 5mgs on day three, which can be
varied at the discretion of the clinician. However, due to the large
inter-individual variation in the response to Warfarin therapy, it is difficult
to identify which patients will reach the narrow therapeutic window for target
INR, and which will be above or below the therapeutic window. The aim of this
research was to develop a methodology using a neural networks classification
algorithm and data mining techniques to predict for a given loading dose and
patient characteristics if the patient is more likely to achieve target INR or
more likely to be above or below therapeutic range.
  Multilayer perceptron (MLP) and 10-fold stratified cross validation
algorithms were used to determine an artificial neural network to classify
patients' response to their initial Warfarin loading dose. The resulting neural
network model correctly classifies an individual's response to their Warfarin
loading dose over 80% of the time. As well as taking into account the initial
loading dose, the final model also includes demographic, genetic and a number
of other potential confounding factors. With this model clinicians can
predetermine whether a given loading regimen, along with specific patient
characteristics will achieve a therapeutic response for a particular patient.
Thus tailoring the loading dose regimen to meet the individual needs of the
patient and reducing the risk of adverse drug reactions associated with
Warfarin.
