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Pacific Rim International Conference on Artificial Intelligence

PRICAI 2016: Trends in Artificial Intelligence: PRICAI 2016 Workshops pp 3–15Cite as

Exploring the Distributional Semantic Relation for ADR and Therapeutic Indication Identification in EMR

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10004))

Abstract

Extraction of relations and their semantic relations from a clinical text is significant to comprehend the actionable harmful and beneficial events between two clinical entities. Particularly to implement drug safety surveillance, two simplest but most important semantic relations are adverse drug reaction and therapeutic indication. In this paper, a method to identify such semantic relations is proposed. A large scale of nearly 1.6 million sentences over 50,998 discharge summary from Electronic Medical Records were preliminary explored. Our approach provided the three main contributions; (i) Electronic Medical Records characteristic exploration; (ii) OpenIE examination for clinical text mining; (iii) automatic semantic relation identification. In this paper, the two complementary information from public knowledge base were introduced as a comparative advantage over expert annotation. Then the set of relation patterns were qualified with 0.05 significant level. The experimental results show that our method can identify the common adverse drug reaction and therapeutic indication with the high lift value. Additionally, a novel adverse drug reaction and alternative drug for a specific symptom therapy are reported to support the comprehensive further drug safety surveillance. The paper clearly illustrates that our method is not only effortless from expert annotation, automatic pattern-specific semantic relation extraction, but also effective for semantic relation identification.

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Notes

  1. 1.

    https://mimic.physionet.org.

  2. 2.

    http://www.rxlist.com/.

  3. 3.

    http://www.mayoclinic.org/.

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Acknowledgment

Authors thank for the research environment, which was supported by National Research University project (NRU) and the Thammasat center of Excellence (CILS), Thailand.

Author information

Authors and Affiliations

  1. Sirindhorn International Institute of Technology, Thammasat University, Klong Luang, Pathum Thani, Thailand

    Siriwon Taewijit & Thanaruk Theeramunkong

  2. Japan Advanced Institute of Science and Technology, Nomi, Ishikawa, Japan

    Siriwon Taewijit

Authors
  1. Siriwon Taewijit
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  2. Thanaruk Theeramunkong
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Corresponding author

Correspondence to Siriwon Taewijit .

Editor information

Editors and Affiliations

  1. ISIR, Osaka University, Ibaraki, Osaka, Japan

    Masayuki Numao

  2. Thammasat University, Bangkadi Muang Pathumthani, Thailand

    Thanaruk Theeramunkong

  3. National Electronics and Computer Technology Center, Language and Semantic Technology Lab, Pathum Thani, Thailand

    Thepchai Supnithi

  4. Information Technology, King Mongkut's University of Technology North Bangkok, Bangkok, Thailand

    Mahasak Ketcham

  5. Mahidol University, Bangkok, Thailand

    Narit Hnoohom

  6. Media Technology, King Mongkut's University of Technology Thonburi, Thonburi, Thailand

    Patiyuth Pramkeaw

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Taewijit, S., Theeramunkong, T. (2017). Exploring the Distributional Semantic Relation for ADR and Therapeutic Indication Identification in EMR. In: Numao, M., Theeramunkong, T., Supnithi, T., Ketcham, M., Hnoohom, N., Pramkeaw, P. (eds) Trends in Artificial Intelligence: PRICAI 2016 Workshops. PRICAI 2016. Lecture Notes in Computer Science(), vol 10004. Springer, Cham. https://doi.org/10.1007/978-3-319-60675-0_1

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  • DOI: https://doi.org/10.1007/978-3-319-60675-0_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-60674-3

  • Online ISBN: 978-3-319-60675-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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