The PULSE project partners at the University of Padova have been working on models to predict the onset of type 2 diabetes and asthma. PULSE can be seen as a big sensor collecting a plethora of heterogeneous data: air pollution, traffic, hours of physical activity, smoking habits, weight, and height, and so on. The models developed by the project can be considered as artificial intelligence (AI) entities that automatically learn from all these data.

The predictive models developed by the EU-funded PULSE project will decide which variables should be considered as risk factors for T2D or asthma onset and will provide a tool to rank subjects based on their risk of developing these diseases.

The team also applied a technique known as 'Bayesian network' (BN) to identify how these variables affect each other and how they might trigger a disease, for example showing how a habit or a particular lifestyle choice affects the probability of developing T2D or asthma.

The combination of predictive models and BN will empower PULSE systems, giving Public Health Observatories the possibility to provide the citizens with specific feedback suggestions on lifestyle and organize interventions based on public health data. Well-being models will also be provided, in order to manage public health problems and promote community health in cities.

Full article : Deterministic and Probabilistic Predictive Modelling of Environmental and Clinical Risk Factors in T2D and Asthma. Authors: E. Longato, A. Zandonà, M. Vettoretti, B. Di Camillo. PULSE project partner, University of Padova.