Health Data ScienceHEAD: 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.
Professor of Public Economics, Luiss University, Rome
Professor of Statistics at the Department of Mathematics, Politecnico di Milano and member of MOX
Full Professor, Economics and Management Politecnico di Milano
With the impressive growth of available data and the flexibility of network modelling, the problem of devising effective quantitative methods for the comparison of networks arises. Plenty of such methods have been designed to accomplish this task: most of them deal with undirected and unweighted networks only, but a few are capable of handling directed […]
Applied sciences have witnessed an explosion of georeferenced data. Object oriented spatial statistics (O2S2) is a recent system of ideas that provides a solid framework where the new challenges posed by the GeoData revolution can be faced, by grounding the analysis on a powerful geometrical and topological approach. We shall present a perspective on O2S2, […]
This paper introduces a new empirical procedure for the estimation of hospitals’ technical efficiency in presence of spatial heterogeneity. We propose a methodology that allows treating spatial heterogeneity independently of a predetermined reference to administrative borders. We define geographical spatial regimes, characterised by spatial proximity and homogeneity of relevant demand characteristics, within which to assess […]
Genome regulatory networks have different layers and ways to modulate cellular processes, such as cell differentiation, proliferation, and adaptation to external stimuli. Transcription factors and other chromatin-associated proteins act as combinatorial protein complexes that control gene transcription. Thus, identifying functional interaction networks among these proteins is a fundamental task to understand the genome regulation framework.