Evaluating Text Analytic Frameworks for Mental Health Surveillance

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....

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Bibliographic Details
Main Authors: Mayer, Benjamin, Arnold, Joshua, Begoli, Edmon, Rush, Everett, Drewry, Michael, Brown, Kris, Ponce, Eduardo, Srinivas, Sudarshan
Format: Conference Proceeding
Language:English
Subjects:
Computational modeling
Data models
information extraction
mental health
Natural language processing
Scalability
Sociology
Statistics
suicide prevention
text analysis
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Summary: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.
ISSN:2473-3490
DOI:10.1109/ICDEW.2018.00014