Evaluating Text Analytic Frameworks for Mental Health Surveillance

Mayer, Benjamin W.; Arnold, Joshua R.; Begoli, Edmon; Rush III, Everett N.; Drewry, Michael F.; Brown, Kris; Brown, Kris A.; Ponce Mojica, Eduardo M.; Srinivasan, Sudarshan

doi:10.1109/ICDEW.2018.00014

Title: Evaluating Text Analytic Frameworks for Mental Health Surveillance

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Abstract

Reducing suicide incidence among US veterans is one of the highest priorities for the US Department of Veterans Affairs (VA). We are implementing a suicide risk detection system, in collaboration with the VA, that would serve as a surveillance system for risk factors appearing in clinical text data. Primary requirements for this system are fast search capability, feature and information extraction, and delivery of data to up-stream natural language processing models. As such, we are evaluating scalable storage solutions on the basis of performance, fault tolerance, and scalability. In this paper we present our current approach to evaluation, preliminary findings, and the work in progress towards a more robust text analysis pipeline.

Authors:

^[1]; Arnold, Joshua R. ^[1];

^[1];

^[1]; Drewry, Michael F. ^[1]; Brown, Kris ^[2]; Brown, Kris A. ^[1]; Ponce Mojica, Eduardo M. ^[1]; Srinivasan, Sudarshan ^[1]

ORNL
University of Tennessee (UT)

Publication Date:: 2018-04-01

Research Org.:: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF)

Sponsoring Org.:: USDOE Office of Science (SC)

OSTI Identifier:: 1465041

DOE Contract Number:: AC05-00OR22725

Resource Type:: Conference

Resource Relation:: Conference: IEEE International Conference on Data Engineering Workshops (ICDEW 2018) - Paris, , France - 4/16/2018 12:00:00 PM-4/20/2018 12:00:00 PM

Country of Publication:: United States

Language:: English

Citation Formats


                    Mayer, Benjamin W., Arnold, Joshua R., Begoli, Edmon, Rush III, Everett N., Drewry, Michael F., Brown, Kris, Brown, Kris A., Ponce Mojica, Eduardo M., and Srinivasan, Sudarshan. Evaluating Text Analytic Frameworks for Mental Health Surveillance.  United States: N. p., 2018. 
        Web.  doi:10.1109/ICDEW.2018.00014.

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                    Mayer, Benjamin W., Arnold, Joshua R., Begoli, Edmon, Rush III, Everett N., Drewry, Michael F., Brown, Kris, Brown, Kris A., Ponce Mojica, Eduardo M., & Srinivasan, Sudarshan. Evaluating Text Analytic Frameworks for Mental Health Surveillance.  United States.  https://doi.org/10.1109/ICDEW.2018.00014

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                    Mayer, Benjamin W., Arnold, Joshua R., Begoli, Edmon, Rush III, Everett N., Drewry, Michael F., Brown, Kris, Brown, Kris A., Ponce Mojica, Eduardo M., and Srinivasan, Sudarshan. 2018.  
        "Evaluating Text Analytic Frameworks for Mental Health Surveillance".  United States.  https://doi.org/10.1109/ICDEW.2018.00014.  https://www.osti.gov/servlets/purl/1465041.

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@article{osti_1465041,

  title        = {Evaluating Text Analytic Frameworks for Mental Health Surveillance},

  author       = {Mayer, Benjamin W. and Arnold, Joshua R. and Begoli, Edmon and Rush III, Everett N. and Drewry, Michael F. and Brown, Kris and Brown, Kris A. and Ponce Mojica, Eduardo M. and Srinivasan, Sudarshan},

  abstractNote = {Reducing suicide incidence among US veterans is one of the highest priorities for the US Department of Veterans Affairs (VA). We are implementing a suicide risk detection system, in collaboration with the VA, that would serve as a surveillance system for risk factors appearing in clinical text data. Primary requirements for this system are fast search capability, feature and information extraction, and delivery of data to up-stream natural language processing models. As such, we are evaluating scalable storage solutions on the basis of performance, fault tolerance, and scalability. In this paper we present our current approach to evaluation, preliminary findings, and the work in progress towards a more robust text analysis pipeline.},

  doi          = {10.1109/ICDEW.2018.00014},

  url          = {https://www.osti.gov/biblio/1465041},
  journal      = {},
number       = ,

  volume       = ,

  place        = {United States},

  year         = {2018},

  month        = {4}

}

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