Simon McDonald

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Three types of data modelling technique are applied retrospectively to individual patients' anticoagulation therapy data to predict their future levels of anticoagulation. The results of the different models are compared and discussed relative to each other and previous similar studies. The conclusions of earlier papers, that machine learning could help(More)
Predicting species distributions with changing climate has often relied on climatic variables, but increasingly there is recognition that disturbance regimes should also be included in distribution models. We examined how changes in rainfall and disturbances along climatic gradients determined demographic patterns in a widespread and long-lived tree(More)
Evolving Takagi Sugeno (eTS) models are optimised for use in applications with high sampling rates. This mode of use produces excellent prediction results very quickly and with low memory requirements, even with large numbers of input attributes. In this paper eTS modelling is adapted for optimality in situations where memory usage and processing time are(More)
The objectives of this work in progress are to improve the levels of care in anticoagulation therapy while reducing the effort required and the costs. This will be achieved by the preprocessing of the available real world data and projecting it into a suitable analysis space before modelling with individualised, constantly learning Evolving Takagi Sugeno(More)
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