Watching the risk factors: Artificial intelligence and the prevention of chronic conditions

Digital healthcare may prevent poor health.

Personalised early risk prediction by artificial intelligence can empower citizens to adopt healthier habits and a better lifestyle.

This project aims at defining a general personalised early risk prediction model that will be used to support individual preventive measures as well as early intervention. New digital tools are designed to empower both citizens and patients. Furthermore, the impact of the new digital tools on health and care pathways are investigated.

Three main scenarios are included: 1. Chronic sun damage and the fight against skin cancer, 2. The late complications of diabetes mellitus and 3. The four main lifestyle risk factors in noncommunicable diseases. In scenario 1, a smartphone application estimates a person`s risk for sun damage and skin cancer.

Both healthy persons and skin cancer patients are included.

The analysis is based on user-collected data indicating previous and current sun exposure, skin type including a computer-based naevus classification and the family history of skin cancer. Persons at increased risk are educated on healthy sun exposure behaviour including sun screen use. In addition, they are asked to see their doctor for a total body skin examination.

In scenario 2, a smartphone application estimates a person`s risk for late complications of diabetes. General lifestyle measures as well as blood sugar levels collected by the patient are used as input for the analysis. Persons at increased risk for complications are given specific advice and are asked to see their doctor.

In scenario 3, a web-based tool to collect general lifestyle data in healthy populations is tested, emphasising the four main risk factors: Unhealthy diet, physical inactivity, tobacco use and harmful use of alcohol.

All data in the project are analysed in a multidisciplinary approach including medical, sociological and behavioural outcomes.