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A Public Health Surveillance Platform Exploiting Free-Text Sources via Natural Language Processing and Linked Data: Application in Adverse Drug Reaction Signal Detection Using PubMed and Twitter

  • Pantelis NatsiavasEmail author
  • Nicos Maglaveras
  • Vassilis Koutkias
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10096)

Abstract

This paper presents a platform enabling the systematic exploitation of diverse, free-text data sources for public health surveillance applications. The platform relies on Natural Language Processing (NLP) and a micro-services architecture, utilizing Linked Data as a data representational formalism. In order to perform NLP in an extendable and modular fashion, the proposed platform employs the Apache Unstructured Information Management Architecture (UIMA) and semantically annotates the results through a newly developed UIMA Semantic Common Analysis Structure Consumer (SCC). The SCC output is a graph represented in the Resource Description Framework (RDF) based on the W3C Web Annotation Data Model (WADM) and SNOMED-CT. We also present the use of the proposed platform through an exemplar application scenario concerning the detection of adverse drug reaction (ADR) signals using data retrieved from PubMed and Twitter.

Keywords

Public health surveillance Micro-services Semantic Web Linked Data Natural Language Processing Adverse drug reactions 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Pantelis Natsiavas
    • 1
    • 2
    Email author
  • Nicos Maglaveras
    • 1
    • 2
  • Vassilis Koutkias
    • 1
    • 2
  1. 1.Lab of Computing and Medical Informatics, Department of MedicineAristotle University of ThessalonikiThessalonikiGreece
  2. 2.Institute of Applied BiosciencesCentre for Research and Technology HellasThermi, ThessalonikiGreece

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