UCLA

The focus of this graduate thesis is the development and optimization of clinically applicable computer-aided diagnostic tools (CADTs) for seizure disorder. This thesis is comprised of two parts (1) development of unimodal and multimodal CADTs for seizure disorder and (2) a novel method for optimization of hyperparameters in machine learning models. The aims of CADTs are to address key challenges in the diagnosis and treatment of seizure disorder, including reducing the time to an accurate diagnosis, improving the sensitivity and specificity of diagnostic neuroimaging, and the understanding of the diagnostic value of interictal scalp electroencephalography (EEG). This could improve the long-term prognosis of patients with non-epileptic seizures (NES) and candidates for potentially curative resective surgery for epilepsy because treatment earlier in these patients'�� disease course has been shown to be more effective. Our novel method for optimizing hyperparameters has the potential to slightly improve the accuracy of machine learning models, while substantially increasing the interpretability of learned estimates and reducing computational cost. We define hyperparameters as variables that contribute to machine learning models but are not optimized jointly with parameters inherent to the model. When viewed as a whole, this body of work represents contributions both to the statistical development and application of machine learning to important clinical challenges.