Personalized quantification of risks in menopausal women with mobile data and statistical machine learning
Prof. Dr. med. Petra Stute / Prof. Dr. David Ginsbourger
Universitätsklinik für Frauenheilkunde, Inselspital / Institut für mathematische Statistik und Versicherungslehre, Universität Bern
Abstract: Menopause affects all women. It is a time when a woman’s body stops producing the sex hormones estrogen and progesterone which usually takes place around the age of 51. As a result, women can have noticeable symptoms like hot flashes, sleeplessness, achy joints, and weight gain. However, more critically menopause can also put women at a greater risk of chronic non-communicable diseases like cardiovascular disease, diabetes mellitus, cancer, osteoporosis, and dementia. This project will develop a digital medical device App called Navina+ for women in or after their menopause. The Navina+ App will work with data from a smart tracker, e.g. FitBit®, to drive statistical machine learning models to predict future risks of chronic disease. The woman will then be given suggestions of action, e.g. life-style changes, hormone replacement therapy etc., depending on the severity and type of risk and the informed self-management choices a woman wishes to make.