Livija Jakaite

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Decision Tree (DT) models are observable for clinical experts and can be used for a probabilistic inference within Bayesian Model Averaging (BMA). The use of Markov Chain Monte Carlo (MCMC) search makes the BMA computationally practical. We employ the MCMC BMA strategy for assessing newborn brain maturity from clinical EEG. Our analysis has revealed that an(More)
Brain development can be evaluated by experts analysing age-related patterns in sleep electroencephalograms (EEG). Natural variations in the patterns, noise, and artefacts affect the evaluation accuracy as well as experts' agreement. The knowledge of predictive posterior distribution allows experts to estimate confidence intervals within which decisions are(More)
Existing Machine Learning (ML) approaches known from the literature require the user to set and experimentally adjust parameters of a decision model to achieve the best result. When artificial neural networks (ANNs) are employed, a typical problem is setting of a proper network structure and learning parameters that are required to minimise possible(More)
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