Markov Logic Networks for Adverse Drug Event Extraction from Text.

Journal Article (Journal Article)

Adverse drug events (ADEs) are a major concern and point of emphasis for the medical profession, government, and society. A diverse set of techniques from epidemiology, statistics, and computer science are being proposed and studied for ADE discovery from observational health data (e.g., EHR and claims data), social network data (e.g., Google and Twitter posts), and other information sources. Methodologies are needed for evaluating, quantitatively measuring, and comparing the ability of these various approaches to accurately discover ADEs. This work is motivated by the observation that text sources such as the Medline/Medinfo library provide a wealth of information on human health. Unfortunately, ADEs often result from unexpected interactions, and the connection between conditions and drugs is not explicit in these sources. Thus, in this work we address the question of whether we can quantitatively estimate relationships between drugs and conditions from the medical literature. This paper proposes and studies a state-of-the-art NLP-based extraction of ADEs from text.

Full Text

Duke Authors

Cited Authors

  • Natarajan, S; Bangera, V; Khot, T; Picado, J; Wazalwar, A; Costa, VS; Page, D; Caldwell, M

Published Date

  • May 2017

Published In

Volume / Issue

  • 51 / 2

Start / End Page

  • 435 - 457

PubMed ID

  • 29123330

Pubmed Central ID

  • PMC5673137

International Standard Serial Number (ISSN)

  • 0219-1377

Digital Object Identifier (DOI)

  • 10.1007/s10115-016-0980-6


  • eng

Conference Location

  • England