Professor Mihaela van der Schaar, ChairWith the advent of electronic health records, more data is continuously collected for individual patients and more data is available for review from past patients.Despite this, it has not yet been possible to successfully use this data to systematically build clinical decision support systems that can produce personalized clinical recommendations to assist clinicians in providing individualized healthcare.In this paper, we present a novel approach, Discovery Engine (DE) that discovers which patient characteristics are most relevant for predicting the correct diagnosis and/or recommending the best treatment regimen for each patient. We demonstrate the performance of DE in two clinical settings: diagnosis of breast cancer as well as personalized recommendation for a specic chemotherapy regimen for breast cancer patients. For each distinct clinical recommendation, dierent patient features are relevant; DE can discover these dierent relevant features and use them to recommend personalized clinical decisions. The DE approach achieves a 16.6% improvement over existing state-of-the-art recommendation algorithms in terms of kappa coecients for recommending the personalized chemotherapy regimens. For diagnostic predictions, the DE approach achieves a 2.18% and 4.20% improvement over existing state-of-the-art prediction algorithms in terms of preii diction error rate and false positive rate, respectively. We also demonstrate that the performance of our approach is robust against missing information, and that the relevant features discovered by DE are conrmed by clinical references.iii The thesis of Jinsung Yoon is approved.

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The prevalence of obesity in the United States approaches half of the adult population. The COVID-19 pandemic endangers the health of obese individuals. In addition, the metabolic syndrome poses a challenge to the health of obese adults. Bariatric surgery and diet restore metabolic homeostasis in obese individuals; however, it is still unclear which strategy is most effective. For example, intermittent fasting improves insulin sensitivity and diet alone decreases visceral adipose tissue at a disproportionately high rate compared to weight loss. Bariatric surgery causes rapid remission of type 2 diabetes and increases incretins for long-term remission of insulin resistance before meaningful weight loss has occurred. Malabsorptive surgeries have provided insight into the mechanism of altering metabolic parameters, but strong evidence to determine the duration of their effects is yet to be established. When determining the best method of weight loss, metabolic parameters, target weight loss, and risk-benefit analysis must be considered carefully. In this review, we address the pros and cons for the optimal way to restore metabolic homeostasis.