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International Conference on E-Technologies

MCETECH 2017: E-Technologies: Embracing the Internet of Things pp 213–221Cite as

Supervised Methods to Support Online Scientific Data Triage

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Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 289))

Abstract

This paper presents machine learning approaches based on supervised methods applied to triage of health and biomedical data. We discuss the applications of such approaches in three different tasks, and evaluate the usage of triage pipelines, as well as data sampling and feature selection methods to improve performance on each task. The scientific data triage systems are based on a generic and light pipeline, and yet flexible enough to perform triage on distinct data. The presented approaches were developed to be integrated as a part of web-based systems, providing real time feedback to health and biomedical professionals. All systems are publicly available as open-source.

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Notes

  1. 1.

    http://www.hivevidence.ca.

  2. 2.

    https://www.ncbi.nlm.nih.gov/mesh.

  3. 3.

    http://clpsych.org/shared-task-2016/.

  4. 4.

    http://nlp.stanford.edu/software/tagger.shtml.

  5. 5.

    http://au.reachout.com/.

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Author information

Authors and Affiliations

  1. Université du Québec à Montréal, Montreal, QC, Canada

    Hayda Almeida, Marc Queudot & Marie-Jean Meurs

  2. Concordia University, Montreal, QC, Canada

    Leila Kosseim

Authors
  1. Hayda Almeida
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  2. Marc Queudot
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  3. Leila Kosseim
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  4. Marie-Jean Meurs
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Corresponding author

Correspondence to Marie-Jean Meurs .

Editor information

Editors and Affiliations

  1. Université de Montréal, Montreal, Québec, Canada

    Esma Aïmeur

  2. University of Ottawa, Ottawa, Ontario, Canada

    Umar Ruhi

  3. Carleton University, Ottawa, Ontario, Canada

    Michael Weiss

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Almeida, H., Queudot, M., Kosseim, L., Meurs, MJ. (2017). Supervised Methods to Support Online Scientific Data Triage. In: Aïmeur, E., Ruhi, U., Weiss, M. (eds) E-Technologies: Embracing the Internet of Things . MCETECH 2017. Lecture Notes in Business Information Processing, vol 289. Springer, Cham. https://doi.org/10.1007/978-3-319-59041-7_13

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  • DOI: https://doi.org/10.1007/978-3-319-59041-7_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59040-0

  • Online ISBN: 978-3-319-59041-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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