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Automatic Extraction of Structured Information from Drug Descriptions

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Mining Intelligence and Knowledge Exploration (MIKE 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11308))

Abstract

This paper describes a Conditional Random Field (CRF) based named entity extraction model that is used for identifying relevant information from drug prescriptions. The entities that the model is able to extract are: dosage, measuring unit, to whom the treatment is directed, frequency and the total duration of treatment. A corpus with 1800 sentences has been compiled and annotated by two experts from drug prescription texts. Using the set of features identified by us, the CRF model hits around 95% F1-measure values for unit, dosage and frequency detection.

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Notes

  1. 1.

    http://transmartfoundation.org.

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Acknowledgments

The work for this paper has been supported in part by the Computer Science Department of the Technical University of Cluj-Napoca, Romania.

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Authors and Affiliations

  1. Department of Computer Science, Technical University of Cluj-Napoca, Bariţiu 28, 400027, Cluj-Napoca, Romania

    Radu Razvan Slavescu & Constantin Maşca

  2. “Iuliu Hatieganu” University of Medicine and Pharmacy, Cluj-Napoca, Romania

    Kinga Cristina Slavescu

Authors
  1. Radu Razvan Slavescu
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  2. Constantin Maşca
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  3. Kinga Cristina Slavescu
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Corresponding author

Correspondence to Radu Razvan Slavescu .

Editor information

Editors and Affiliations

  1. Technical University of Cluj-Napoca, Cluj-Napoca, Romania

    Adrian Groza

  2. Indian Institute of Information Technology, Sri City, India

    Rajendra Prasath

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Slavescu, R.R., Maşca, C., Slavescu, K.C. (2018). Automatic Extraction of Structured Information from Drug Descriptions. In: Groza, A., Prasath, R. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2018. Lecture Notes in Computer Science(), vol 11308. Springer, Cham. https://doi.org/10.1007/978-3-030-05918-7_3

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  • DOI: https://doi.org/10.1007/978-3-030-05918-7_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-05917-0

  • Online ISBN: 978-3-030-05918-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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