Text mining MEDLINE to support public health

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

Research output: Contribution to conferencePaper

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Abstract

Today’s society is data rich and information driven, with access
to numerous data sources available that have the potential to
provide new insights into areas such as disease prevention,
personalised medicine and data driven policy decisions. This
paper describes and demonstrates the use of text mining tools
developed to support public health institutions to complement
their data with other accessible open data sources, optimize
analysis and gain insight when examining policy. In particular
we focus on the exploration of MEDLINE, the biggest
structured open dataset of biomedical knowledge. In
MEDLINE we utilize its terminology for indexing and
cataloguing biomedical information – MeSH – to maximize the
efficacy of the dataset.
Original languageEnglish
Publication statusPublished - 11 Oct 2018
EventSiKDD 2018: Conference on Data Mining and Data Warehouses - Ljubljana, Slovenia
Duration: 11 Oct 2018 → …
https://ailab.ijs.si/dunja/SiKDD2018/

Conference

ConferenceSiKDD 2018
CountrySlovenia
CityLjubljana
Period11/10/18 → …
Internet address

Keywords

  • Big Data
  • Public Health
  • Healthcare
  • Text Mining
  • Machine Learning
  • MEDLINE
  • MeSH Headings

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    Pita Costa, J., Stopar, L., Fuart, F., Grobelnik, M., Santanam, R., Sun, C., Carlin, P., Black, M., & Wallace, J. G. (2018). Text mining MEDLINE to support public health. Paper presented at SiKDD 2018, Ljubljana, Slovenia.