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.
| Original language | English |
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| Title of host publication | Proceedings - IEEE 34th International Conference on Data Engineering Workshops, ICDEW 2018 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 39-47 |
| Number of pages | 9 |
| ISBN (Electronic) | 9781538663066 |
| DOIs | |
| State | Published - Jul 2 2018 |
| Event | 34th IEEE International Conference on Data Engineering Workshops, ICDEW 2018 - Paris, France Duration: Apr 16 2018 → Apr 19 2018 |
Publication series
| Name | Proceedings - IEEE 34th International Conference on Data Engineering Workshops, ICDEW 2018 |
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Conference
| Conference | 34th IEEE International Conference on Data Engineering Workshops, ICDEW 2018 |
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| Country/Territory | France |
| City | Paris |
| Period | 04/16/18 → 04/19/18 |
Funding
This manuscript has been in part co-authored by UTBattelle, LLC under Contract No. DE-AC05-00OR22725, and under a joint program with the Department of Veterans Affairs under the Million Veteran Project Computational Health Analytics for Medical Precision to Improve Outcomes Now (MVP-CHAMPION). The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a nonexclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doepublic-access-plan). This research used resources of the Compute and Data Environment for Science (CADES) at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725. This research used resources of the Compute and Data Environment for Science (CADES) at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725. ACKNOWLEDGMENT This manuscript has been in part co-authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725, and under a joint program with the Department of Veterans Affairs under the Million Veteran Project Computational Health Analytics for Medical Precision to Improve Outcomes Now (MVP-CHAMPION). The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a nonexclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).
Keywords
- Information extraction
- Mental health
- Natural language processing
- Suicide prevention
- Text analysis
