Health Data Science
HEAD: Emanuele Di AngelantonioThe 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
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Emanuele Di Angelantonio
Head of Health Data Science Centre -
Emanuele Rabosio
Postdoctoral Researcher -
Francesca Ieva
Associate Professor of Statistics at MOX - Modeling and Scientific Computing laboratory, Department of Mathematics, Politecnico di Milano
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:
- Letizia Clementi, Brain Connectivity - Functional Data Analysis - Neuroimaging - Volumetric Domains
Advisor: Marco Domenico Santambrogio - Federica Corso, Health Analytics - Multivariate Statistics - Machine Learning Causal Inference - Cost-Benefit analysis
Advisor: Anna Maria Paganoni - Laura Savaré, Real World data - Pharmacoepidemiology - Risk stratification - Survival analysis - Analysis of the sequences
Advisor: Francesca Ieva
2nd year:
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Riccardo Scimone, Data Science - Spatial Statistics - Functional Data - Object Oriented Statistics
Advisor: Piercesare Secchi -
Francesco Scotti, Regional Science - Structural and Cohesion Funds - Socio-economic development - COVID-19 pandemic - Economic Resilience
Advisor: Fabio Pammolli -
Valeria Maria Urbano, Data analytics - Case study - Survey - Experimental research
Advisor: Giovanni Azzone
3rd year:
- Fabio Azzalini, Data Management - Data Integration - Computer Ethics
Advisor: Letizia Tanca - Michela Carlotta Massi, Statistics - Machine Learning - Statistical Genomics - Representation Learning - Computational Biology
Advisor: Francesca Ieva - Agostino Torti, Functional Data Analysis – Network Analysis – Mobility Data
Advisor: Piercesare Secchi
Steering committee
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Munther Dahleh
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Alistair McGuire
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Mauro Marè
Professor of Public Economics, Luiss University, Rome -
Piercesare Secchi
Professor of Statistics at the Department of Mathematics, Politecnico di Milano and member of MOX -
Fabio Pammolli
Full Professor, Economics and Management Politecnico di Milano -
Andrea Cavalli
Publications
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09/2021 - Elsevier
A look at the spatio-temporal mortality patterns in Italy during the COVID-19 pandemic through the lens of mortality densities
With the tools and perspective of Object Oriented Spatial Statistics, we analyze official daily data on mortality from all causes in the provinces and municipalities of Italy for the year 2020, the first of the COVID-19 pandemic. By comparison with mortality data from 2011 to 2019, we assess the local impact of the pandemic as […]
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08/2021 - Journal of the Royal Statistical Society
Social and material vulnerability in the face of seismic hazard: An analysis of the Italian case
The assessment of the vulnerability of a community endangerd by seismic hazard is of paramount importance for planning a precision policy aimed at the prevention and reduction of its seismic risk. We aim at measuring the vulnerability of the Italian municipalities exposed to seismic hazard, by analysing the open data offered by the Mappa dei […]
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07/2020 - BMC Medical Informatics and Decision Making
Data mining application to healthcare fraud detection: a two-step unsupervised clustering method for outlier detection with administrative databases
Being the recipient for huge public and private investments, the healthcare sector results to be an interesting target for fraudsters. Nowadays, the availability of a great amount of data makes it possible to tackle this issue with the adoption of data mining techniques. This approach can provide more efficient control of processes in terms of […]
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06/2020 - Quantitative Science Studies
Connected from the Outside: The Role of US Regions in Promoting the Integration of the European Research System
Considerable efforts have been deployed by the European Union to create an integrated Research & Development area. In this paper, we focus on the structure and evolution of the European collaboration network as reflected by patent data. We study patent networks representing collaborations between inventors located in different geographic areas. Existing studies seem to indicate […]
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06/2020 - Springer
Modeling the Effect of Recurrent Events on Time-to-event Processes by Means of Functional Data
In this paper we propose a methodological framework for modeling information carried out by a longitudinal process by means of functional data, within a survival framework targeting the time-to-event process of interest. In particular, the longitudinal process is represented by the compensator of a marked point process the recurrent events are supposed to derive from. […]
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