Text mining the EMR for modeling and predicting suicidal behavior among US veterans of the 1991 Persian gulf war

Alon Ben-Ari, Kenric Hammond

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Scopus citations

Abstract

Suicide is an important public health problem and prominent among U.S. Veterans and active duty troops. Prediction of suicide and suicide attempts is problematic because these are low-frequency events and traditional clinical screening approaches have a high false positive rate. Large clinical databases extracted from electronic health records permit study of suicidal behavior in larger populations than previously possible using sampling techniques. In addition to offering structured data, text search and classification methods can identify additional risk variables. Data extracted from clinical records of 250,000 veterans were modeled using machine learning methodology. To predict suicide attempts in this population over a 10 year period. In contrast to previously reported models, our results showed high specificity and a false positive rate of 0.5%, contrasting with other studies showing false positive rates exceeding 20%. The model showed lower specificity with a true positive rate of 27% and a false negative rate of 73%. These results suggest that a machine learning approach developed with large data sets can usefully supplement current approaches to prediction of suicidal behavior.

Original languageEnglish (US)
Title of host publicationProceedings of the 48th Annual Hawaii International Conference on System Sciences, HICSS 2015
PublisherIEEE Computer Society
Pages3168-3175
Number of pages8
Volume2015-March
ISBN (Electronic)9781479973675
DOIs
StatePublished - Jan 1 2015
Externally publishedYes
Event48th Annual Hawaii International Conference on System Sciences, HICSS 2015 - Kauai, United States
Duration: Jan 5 2015Jan 8 2015

Other

Other48th Annual Hawaii International Conference on System Sciences, HICSS 2015
CountryUnited States
CityKauai
Period1/5/151/8/15

Keywords

  • Text mining

ASJC Scopus subject areas

  • Engineering(all)

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    Ben-Ari, A., & Hammond, K. (2015). Text mining the EMR for modeling and predicting suicidal behavior among US veterans of the 1991 Persian gulf war. In Proceedings of the 48th Annual Hawaii International Conference on System Sciences, HICSS 2015 (Vol. 2015-March, pp. 3168-3175). [7070197] IEEE Computer Society. https://doi.org/10.1109/HICSS.2015.382