The application of a perceptron model to classify an individual's   response to a proposed loading dose regimen of Warfarin
  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. 
Cen Wan, Irina V. Biktasheva, Steven Lane
stat.AP cs.NE