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

The application of a perceptron model to classify an individual's response to a proposed loading dose regimen of Warfarin