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Markov logic networks for adverse drug event extraction from text

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Abstract

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.

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Notes

  1. http://www.imi-protect.eu/.

  2. http://omop.org/.

  3. http://imeds.reaganudall.org/.

  4. http://www.health.gov/hai/pdfs/ADE-Action-Plan-508c.

  5. A clique in a graph is a fully connected sub-graph of the original graph. A triangle is a clique of size 3, an edge is of size 2 and a fully connected square with both diagonals if of size 4.

  6. http://omop.fnih.org/sites/default/files/ground%20truth.

  7. If there are less than 50 abstracts for a particular ADE pair, we use only the returned set of documents.

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Acknowledgments

The authors gratefully acknowledge National Institute of Health Grant Number NIGMS 5R01GM097618 for the support.

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Correspondence to Sriraam Natarajan.

Appendix

Appendix

See Tables 6, 7 and 8.

Table 6 List of predicates in MLN
Table 7 Rules to deduce adverseC predicates, which subsequently influence the posterior probability of the adverse predicate
Table 8 Final MLN Rules

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Natarajan, S., Bangera, V., Khot, T. et al. Markov logic networks for adverse drug event extraction from text. Knowl Inf Syst 51, 435–457 (2017). https://doi.org/10.1007/s10115-016-0980-6

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