Data science approaches to study epidemiological and genetic underpinnings of hypothyroidism to pave the way for precision medicine
Autoimmune hypothyroidism is one of the most common endocrine diseases worldwide. Having hypothyroidism affects the quality of life of the patients and increases their likelihood for having other adverse health conditions including cardiovascular, metabolic, mental and fertility conditions as well as thyroid cancer and other immune-mediated diseases. Hence, there is a critical need for understanding the patient profiles to offer improved personalized healthcare and life-style recommendations to individuals at risk. In this project, I aim to shed new light on the underlying causes and progression of the disease by analysing large patient data sets from Denmark and abroad, including genetic factors and other molecular biomarkers. I will use and develop bioinformatics and machine learning methods to define disease trajectories, to look for patient subgroups, and to understand the genetic and non-genetic underpinnings of the disease.