Congenital hypothyroidism (CH) is a condition of severe thyroid hormone (TH) deficiency present at birth. The current treatment is clear, and straightforward - a daily oral dose of thyroxine (T4). But, knowing precisely how much T4 to give and how to adjust dosages over time is an unsolved major treatment issue. Too much can produce side effects throughout life; too little is not enough to sustain normal growth and development. We developed and quantified a non-linear, multi-compartmental CH neonate model, as an adaptation of an existing simulator of feedback control of human TH levels in children and adults. Using a simulation approach, we found that simulated L-T4 plus L-T3 treatment is better than L-T4 alone treatment and the best treatment for CH babies is 1st 8 mcg/kg L-T4, followed by 0.5 mcg/kg/day L-T4, with 0.10 mcg/kg/day L-T3 over the first month of life - the period for which we had reliable data.

Stroke is the fifth leading cause of death in the United States, with approximately 795,000 new cases each year. The goal of stroke treatment is to rescue salvageable tissue by reperfusion therapy. Clinical trials have shown that intravenous tissue plasminogen activator (IV tPA) and clot retrieval devices are effective treatments for recanalizing occluded blood vessels. However, determining an optimal stroke treatment plan is not a straightforward decision because it involves different factors, such as patient risk of hemorrhage and penumbra size. The relationships between these factors and patient outcomes are not clearly understood. This dissertation attempts to overcome these challenges by developing machine learning and deep learning models for acute ischemic stroke clinical and imaging outcome classification. A novel deep learning model was first proposed using source perfusion imaging to predict voxel-wise tissue outcome. The model architecture is designed to include contralateral patches to improve the feature learning process. Second, an end-to-end machine learning approach was developed to classify stroke onset time, which is a major clinical variable in selecting patients for IV tPA treatments. The approach combines baseline descriptive features and deep features to improve stroke onset time classification using machine learning models. Third, a bi-input convolutional neural network was developed for perfusion parameter estimation. This model lays a foundation to estimate perfusion parameters using pattern recognition techniques. Finally, a machine learning model trained with a balanced data set was developed for acute stroke patient outcome prediction. Rigorous experiments and results have shown the effectiveness of these proposed methods. This dissertation describes methods that lead to better understanding of stroke imaging, which lays the foundation to offer decision-making guidance for clinicians providing acute stroke intervention treatments.