There is an ever increasing number of data sources that potentially could be used to gain new insights into areas such as disease prevention, policy formulation/ evaluation and personalised medicine, but these are not optimised for use within an analytics type user interface. The MIDAS project was funded under a call for ‘Big Data supporting Public Health policies’ to develop a big data platform that facilitates the utilisation of healthcare data beyond existing isolated systems, making that data amenable to enrichment with open and social data . This aligns closely with a number of themes in Knowledge Discovery in Databases (KDD) in that the platform enables the integration of heterogeneous data sources, providing privacy-preserving analytics, forecasting tools and visualisation modules to deliver actionable information. Policy makers as a result will have the capability to perform data-driven evaluations of the efficiency and effectiveness of proposed policies in terms of expenditure, delivery, wellbeing, and health and socio-economic inequalities, thus improving current policy formulation, delivery risk stratification and evaluation. This H2020 project has a total of 15 partners from 5 EU countries as well as Arizona State University (ASU). The partners are Universities, SMEs and health departments in governmental institutions.
|Number of pages||2|
|Publication status||Accepted/In press - 15 Jun 2018|
|Event||KDD 2018 (24th ACM SIGKDD Conference on Knowledge Discovery and Data Mining) - |
Duration: 19 Aug 2018 → 23 Aug 2018
|Conference||KDD 2018 (24th ACM SIGKDD Conference on Knowledge Discovery and Data Mining)|
|Period||19/08/18 → 23/08/18|
Pita Costa, J., Stopar, L., Fuart, F., Grobelnik, M., Santanam, R., Sun, C., ... Wallace, J. G. (Accepted/In press). Mining Medline for the Visualisation of a Global Perspective on Biomedical Knowledge. Poster session presented at KDD 2018 (24th ACM SIGKDD Conference on Knowledge Discovery and Data Mining), .