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

Steering Committee

Publications

  • 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 […]

  • 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 […]

  • 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. […]

  • 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 […]