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

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.
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

Fingerprint

Visualization
Health
Public health
User interfaces
Medicine
Data mining
Economics
Big data

Cite this

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), .
Pita Costa, João ; Stopar, Luka ; Fuart, Flavio ; Grobelnik, Marko ; Santanam, Raghu ; Sun, Chenlu ; Carlin, Paul ; Black, Michaela ; Wallace, J. G. / 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), .2 p.
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Pita Costa, J, Stopar, L, Fuart, F, Grobelnik, M, Santanam, R, Sun, C, Carlin, P, Black, M & Wallace, JG 2018, 'Mining Medline for the Visualisation of a Global Perspective on Biomedical Knowledge' KDD 2018 (24th ACM SIGKDD Conference on Knowledge Discovery and Data Mining), 19/08/18 - 23/08/18, .

Mining Medline for the Visualisation of a Global Perspective on Biomedical Knowledge. / Pita Costa, João; Stopar, Luka; Fuart, Flavio; Grobelnik, Marko; Santanam, Raghu; Sun, Chenlu; Carlin, Paul; Black, Michaela; Wallace, J. G.

2018. Poster session presented at KDD 2018 (24th ACM SIGKDD Conference on Knowledge Discovery and Data Mining), .

Research output: Contribution to conferencePoster

TY - CONF

T1 - Mining Medline for the Visualisation of a Global Perspective on Biomedical Knowledge

AU - Pita Costa, João

AU - Stopar, Luka

AU - Fuart, Flavio

AU - Grobelnik, Marko

AU - Santanam, Raghu

AU - Sun, Chenlu

AU - Carlin, Paul

AU - Black, Michaela

AU - Wallace, J. G.

N1 - no evidence of it online.

PY - 2018/6/15

Y1 - 2018/6/15

N2 - 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.

AB - 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.

M3 - Poster

ER -

Pita Costa J, Stopar L, Fuart F, Grobelnik M, Santanam R, Sun C et al. Mining Medline for the Visualisation of a Global Perspective on Biomedical Knowledge. 2018. Poster session presented at KDD 2018 (24th ACM SIGKDD Conference on Knowledge Discovery and Data Mining), .