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

  • 06/2020 - Springer

    O2S2 for the Geodata Deluge

    We illustrate a fewrecent ideas of Object Oriented Spatial Statistics (O2S2), focusing on the problem of kriging prediction in situations where a global second order stationarity assumption for the random field generating the data is not justifiable or the space domain of the field is complex. By localizing the analysis through the Random Domain Decomposition […]

  • 06/2020 - Proceedings of the National Academy of Sciences

    Economic and social consequences of human mobility restrictions under COVID-19

    In response to the coronavirus disease 2019 (COVID-19) pandemic, several national governments have applied lockdown restrictions to reduce the infection rate. Here we perform a massive analysis on near–real-time Italian mobility data provided by Facebook to investigate how lockdown strategies affect economic conditions of individuals and local governments. We model the change in mobility as […]

  • 06/2020 - BMC Health Services Research

    Evaluating the effect of healthcare providers on the clinical path of Heart Failure patients through a novel semi-Markov multi-state model

    Investigating similarities and differences among healthcare providers, on the basis of patient healthcare experience, is of interest for policy making. Availability of high quality, routine health databases allows a more detailed analysis of performance across multiple outcomes, but requires appropriate statistical methodology.

  • 06/2020 - European Journal of Hybrid Imaging

    Methodological framework for radiomics applications in Hodgkin’s Lymphoma

    According to published data, radiomics features differ between lesions of refractory/relapsing HL patients from those of long-term responders. However, several methodological aspects have not been elucidated yet.

  • 04/2020 - Journal of Translational Medicine

    The Endless Frontier? The Recent Increase of R&D Productivity in Pharmaceuticals

    Studies on the early 2000s documented increasing attrition rates and duration of clinical trials, leading to a representation of a “productivity crisis” in pharmaceutical research and development (R&D). In this paper, we produce a new set of analyses for the last decade and report a recent increase of R&D productivity within the industry.