- By:
- Mayer, Benjamin W; Arnold, Joshua R; Begoli, Edmon ; Rush, Everett N; Drewry, Michael F; Brown, Kris; Brown, Kris A; Ponce Mojica, Eduardo M; Srinivasan, Sudarshan
- Journal Name:
- International Conference on Data Engineering Workshops
- Page Number:
- 39-47
- Volume:
- 1
- Issue Number:
- 1
- Publication Date:
- August 14, 2018
- Conference Name:
- IEEE International Conference on Data Engineering Workshops (ICDEW 2018)
- Conference Location:
- Paris, France
- Conference Sponsor:
- IEEE
- View DOI Listing:
- https://doi.org/10.1109/ICDEW.2018.00014
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.
