AI-based long-term health risk evaluation for driving behaviour change strategies in children and youth

Non-communicable diseases (NCDs) are the leading cause of death and healthcare expense. Common risk factors for many of them are obesity and low physical fitness resulting from an unhealthy lifestyle.

Targeting children and youth for lifestyle interventions has been suggested because (1) early precursors of most NCDs are already present at this age, (2) childhood and adolescence are critical periods for the acquisition of healthy lifestyle habits, and (3) unhealthy lifestyle in this age group is prevalent.We propose to develop long-term risk-prediction models for cardiovascular and metabolic disease for people aged 5–19.

We have already identified 15 datasets with data on behaviour, fitness, biomarkers and actual NCDs spanning various ages.

We will develop machine-learning methods that can train models on such heterogeneous datasets, enabling the prediction of risk for people of various ages for whom different data is available.

We will employ federated learning for data privacy, carefully curate and balance the data to ensure it is bias-free and representative of the target group, and employ methods for explanation and visualisation of the data, models and predictions.

Participatory design involving explanation of the AI will be used to design two applications: one for health professionals and the other for citizens.

Both will show the risks broken down by risk factors, and the recommended behaviour changes to reduce them, in a manner appropriate for each user group.

The developed solution will be validated in a large proof-of-concept study in four countries involving different health settings (family, school, primary care, integrated care …).To facilitate practical use of the developed solution, we will prepare recommendations for their implementation, and a realistic exploitation plan.

These activities will be supported by dissemination and communication activities specifically tailored to the target groups (e.g., involving science museums).