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

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 conference › Paper › peer-review

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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 language	English
Publication status	Published (in print/issue) - 11 Oct 2018
Event	SiKDD 2018: Conference on Data Mining and Data Warehouses - Ljubljana, Slovenia Duration: 11 Oct 2018 → … https://ailab.ijs.si/dunja/SiKDD2018/

Conference

Conference	SiKDD 2018
Country/Territory	Slovenia
City	Ljubljana
Period	11/10/18 → …
Internet address	https://ailab.ijs.si/dunja/SiKDD2018/

Keywords

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

SiKDD2018Final published version, 1.09 MB

Cite this

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title = "Text mining MEDLINE to support public health",

abstract = "Today{\textquoteright}s society is data rich and information driven, with accessto numerous data sources available that have the potential toprovide new insights into areas such as disease prevention,personalised medicine and data driven policy decisions. Thispaper describes and demonstrates the use of text mining toolsdeveloped to support public health institutions to complementtheir data with other accessible open data sources, optimizeanalysis and gain insight when examining policy. In particularwe focus on the exploration of MEDLINE, the biggeststructured open dataset of biomedical knowledge. InMEDLINE we utilize its terminology for indexing andcataloguing biomedical information – MeSH – to maximize theefficacy of the dataset.",

keywords = "Big Data, Public Health, Healthcare, Text Mining, Machine Learning, MEDLINE, MeSH Headings",

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T1 - Text mining MEDLINE to support public health

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.

PY - 2018/10/11

Y1 - 2018/10/11

N2 - Today’s society is data rich and information driven, with accessto numerous data sources available that have the potential toprovide new insights into areas such as disease prevention,personalised medicine and data driven policy decisions. Thispaper describes and demonstrates the use of text mining toolsdeveloped to support public health institutions to complementtheir data with other accessible open data sources, optimizeanalysis and gain insight when examining policy. In particularwe focus on the exploration of MEDLINE, the biggeststructured open dataset of biomedical knowledge. InMEDLINE we utilize its terminology for indexing andcataloguing biomedical information – MeSH – to maximize theefficacy of the dataset.

AB - Today’s society is data rich and information driven, with accessto numerous data sources available that have the potential toprovide new insights into areas such as disease prevention,personalised medicine and data driven policy decisions. Thispaper describes and demonstrates the use of text mining toolsdeveloped to support public health institutions to complementtheir data with other accessible open data sources, optimizeanalysis and gain insight when examining policy. In particularwe focus on the exploration of MEDLINE, the biggeststructured open dataset of biomedical knowledge. InMEDLINE we utilize its terminology for indexing andcataloguing biomedical information – MeSH – to maximize theefficacy of the dataset.

KW - Big Data

KW - Public Health

KW - Healthcare

KW - Text Mining

KW - Machine Learning

KW - MEDLINE

KW - MeSH Headings

UR - https://ailab.ijs.si/dunja/SiKDD2018/

M3 - Paper

T2 - SiKDD 2018

Y2 - 11 October 2018

ER -

Text mining MEDLINE to support public health

Abstract

Conference

Keywords

UN SDGs

Access to Document

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Fingerprint

Michaela Black

Cite this

Text mining MEDLINE to support public health

Abstract

Conference

Keywords

UN SDGs

Access to Document

Other files and links

Fingerprint

Profiles

Michaela Black

Cite this