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Personalized Medical Reading Recommendation: Deep Semantic Approach

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Database Systems for Advanced Applications (DASFAA 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9645))

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Abstract

Therapists are faced with the overwhelming task of identifying, reading, and incorporating new information from a vast and fast growing volume of publications into their daily clinical decisions. In this paper, we propose a system that will semantically analyze patient records and medical articles, perform medical domain specific inference to extract knowledge profiles, and finally recommend publications that best match with a patient’s health profile. We present specific knowledge extraction and matching details, examples, and results from the mental health domain.

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Author information

Authors and Affiliations

  1. Lymba Corporation, 901 Waterfall Way, Bldg 5, Richardson, TX, 75080, USA

    Tatiana Erekhinskaya, Mithun Balakrishna, Marta Tatu & Dan Moldovan

Authors
  1. Tatiana Erekhinskaya
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  2. Mithun Balakrishna
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  3. Marta Tatu
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  4. Dan Moldovan
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Corresponding author

Correspondence to Tatiana Erekhinskaya .

Editor information

Editors and Affiliations

  1. Harbin Institute of Technology, Harbin, China

    Hong Gao

  2. Kangwon National University, Kangwon, Korea (Republic of)

    Jinho Kim

  3. Kumamoto University, Kumamoto-shi, Japan

    Yasushi Sakurai

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© 2016 Springer International Publishing Switzerland

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Erekhinskaya, T., Balakrishna, M., Tatu, M., Moldovan, D. (2016). Personalized Medical Reading Recommendation: Deep Semantic Approach. In: Gao, H., Kim, J., Sakurai, Y. (eds) Database Systems for Advanced Applications. DASFAA 2016. Lecture Notes in Computer Science(), vol 9645. Springer, Cham. https://doi.org/10.1007/978-3-319-32055-7_8

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  • DOI: https://doi.org/10.1007/978-3-319-32055-7_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-32054-0

  • Online ISBN: 978-3-319-32055-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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