2 research outputs found
A Computer-Aided System for Determining the Application Range of a Warfarin Clinical Dosing Algorithm Using Support Vector Machines with a Polynomial Kernel Function
Determining the optimal initial dose for warfarin is a critically important
task. Several factors have an impact on the therapeutic dose for individual
patients, such as patients' physical attributes (Age, Height, etc.), medication
profile, co-morbidities, and metabolic genotypes (CYP2C9 and VKORC1). These
wide range factors influencing therapeutic dose, create a complex environment
for clinicians to determine the optimal initial dose. Using a sample of 4,237
patients, we have proposed a companion classification model to one of the most
popular dosing algorithms (International Warfarin Pharmacogenetics Consortium
(IWPC) clinical model), which identifies the appropriate cohort of patients for
applying this model. The proposed model functions as a clinical decision
support system which assists clinicians in dosing. We have developed a
classification model using Support Vector Machines, with a polynomial kernel
function to determine if applying the dose prediction model is appropriate for
a given patient. The IWPC clinical model will only be used if the patient is
classified as "Safe for model". By using the proposed methodology, the dosing
mode's prediction accuracy increases by 15 percent in terms of Root Mean
Squared Error and 17 percent in terms of Mean Absolute Error in dose estimates
of patients classified as "Safe for model".Comment: 6 pages, 8 tables, 1 figur
Warfarin dose estimation on multiple datasets with automated hyperparameter optimisation and a novel software framework
Warfarin is an effective preventative treatment for arterial and venous
thromboembolism, but requires individualised dosing due to its narrow
therapeutic range and high individual variation. Many machine learning
techniques have been demonstrated in this domain. This study evaluated the
accuracy of the most promising algorithms on the International Warfarin
Pharmacogenetics Consortium dataset and a novel clinical dataset of South
African patients. Support vectors and linear regression were amongst the top
performers in both datasets and performed comparably to recent stacked ensemble
approaches, whilst neural networks were one of the worst performers in both
datasets. We also introduced genetic programming to automatically optimise
model architectures and hyperparameters without human guidance. Remarkably, the
generated models were found to match the performance of the best models
hand-crafted by human experts. Finally, we present a novel software framework
(Warfit-learn) for warfarin dosing research. It leverages the most successful
techniques in preprocessing, imputation, and parallel evaluation, with the goal
of accelerating research and making results in this domain more reproducible.Comment: 19 pages, 4 tables, 3 figure