Evaluating Text Analytic Frameworks for Mental Health Surveillance

Conference · Sun Apr 01 00:00:00 EDT 2018

DOI:https://doi.org/10.1109/ICDEW.2018.00014· OSTI ID:1465041

Mayer, Benjamin W.  ^[1]; Arnold, Joshua R. ^[1]; Begoli, Edmon  ^[1]; Rush III, Everett N.  ^[1]; Drewry, Michael F. ^[1]; Brown, Kris ^[2]; Brown, Kris A. ^[1]; Ponce Mojica, Eduardo M. ^[1]; Srinivasan, Sudarshan ^[1]

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1. ORNL
2. University of Tennessee (UT)

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.

• Research Organization: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF)
• Sponsoring Organization: USDOE Office of Science (SC)
• DOE Contract Number: AC05-00OR22725
• OSTI ID: 1465041
• 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