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.
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)
- 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
- 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
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
06/2020 - Springer
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
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
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
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.