Mining Medline for the Visualisation of a Global Perspective on Biomedical Knowledge

João Pita Costa, Luka Stopar, Flavio Fuart, Marko Grobelnik, Raghu Santanam, Chenlu Sun, Paul Carlin, Michaela Black, J. G. Wallace

Research output: Contribution to conferencePoster

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Abstract

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.
Original languageEnglish
Number of pages2
Publication statusAccepted/In press - 15 Jun 2018
EventKDD 2018 (24th ACM SIGKDD Conference on Knowledge Discovery and Data Mining) -
Duration: 19 Aug 201823 Aug 2018

Conference

ConferenceKDD 2018 (24th ACM SIGKDD Conference on Knowledge Discovery and Data Mining)
Period19/08/1823/08/18

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    Pita Costa, J., Stopar, L., Fuart, F., Grobelnik, M., Santanam, R., Sun, C., Carlin, P., Black, M., & 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), . https://www.kdd.org/kdd2018/files/KDD_2018_Booklet_-_Aug_3.pdf