Title of project

Modelling and Algorithms for Optimizing Obesity Treatment on GLP1

Abstract

The obesity epidemic is a growing global health issue, with over a billion people expected to be living with obesity by 2030. Proper weight management can greatly reduce the risks of obesity related complications, and GLP-1 receptor agonists have shown promise in improving glycemic control and aiding weight loss. Unfortunately, many patients will not realize the full potential of the drug due to early discontinuation. Discontinuations may be due to side effects or a lack of understanding of the treatment benefits, especially during the early and escalation phases. We aim to improve adherence rates among GLP-1 users by personalizing the treatment, as opposed to the current one-size-fits-all approach. We will develop algorithms for Health Care Practitioners that use patient-specific data to tailor the treatment to the individual patient on anti-obesity medication, thereby minimizing possible side effects during the most critical phases and maximizing the clinical outcomes. We will furthermore develop algorithms capable to provide predictions, information and guidance directly to the patient, helping them stay involved in their own treatment process. Such tools will lead to better patient experiences, which in turn will improve adherence and ultimately result in enhanced clinical outcomes.

To develop such tools, the project will leverage physiological modelling, scientific computing, and various machine learning techniques. We will model the metabolic processes related to obesity and weight loss and investigate the possible correlations between different patient parameters and the risk of experiencing different side effects.