Health Data Science

HEAD: Emanuele Di Angelantonio

The Centre for Health Data Science aims to become a reference institution for the analysis of healthcare data. It will collect data and information from a variety of sources by establishing a dialogue with regional healthcare districts, hospitals and scientific societies. The Centre will integrate clinical data with socioeconomic and environmental risk factors to identify precise vulnerability profiles in order to create targeted policy interventions. In addition, it will work to develop solutions for the analysis of data, developing and integrating new analytical methods with clinical epidemiology and healthcare research.

Centre members

DADS PhD Programme

Through a collaboration with Politecnico di Milano, the Health Data Science Centre promotes a PhD programme in Data Analytics and Decision Sciences (DADS)

1st year:

2nd year:

3rd year:

Steering committee

Publications

  • 06/2018 - Proc. Intl. Soc. Mag. Reson. Med.

    Tractography–Based Atlas of the Healthy Cortico-Spinal Tract

    The corticospinal tract is a critical white matter pathway as it connects the primary motor cortex to the spinal cord and handles voluntary motion. Atlases of major brain connections do exist, but, surprisingly, atlases that depict the actual localisation of a specific pathway are missing. In this work, we propose a comprehensive statistical methodology for […]

  • 05/2018 - Statistics & Probability Letters

    On the role of statistics in the era of big data: A call for a debate

    While discussing the plenary talk of Dunson (2016) at the 48th Scientific Meeting of the Italian Statistical Society, I formulated a few general questions on the role of statistics in the era of big data which stimulated an interesting debate. They are reported here with the aim of engaging a larger audience on an issue […]

  • 11/2017 - Scientific Reports

    On Economic Complexity and the Fitness of Nations

    Complex economic systems can often be described by a network, with nodes representing economic entities and edges their interdependencies, while network centrality is often a good indicator of importance. Recent publications have implemented a nonlinear iterative Fitness-Complexity (FC) algorithm to measure centrality in a bipartite trade network, which aims to represent the ‚ÄėFitness‚Äô of national […]