DocumentCode :
265000
Title :
Application of a Hybrid Text Mining Approach to the Study of Suicidal Behavior in a Large Population
Author :
Hammond, Kenric W. ; Laundry, Ryan J.
Author_Institution :
Univ. of Washington, Seattle, WA, USA
fYear :
2014
fDate :
6-9 Jan. 2014
Firstpage :
2555
Lastpage :
2561
Abstract :
To fulfill the promise of electronic health records to support the study of disease in populations, efficient techniques are required to search large clinical corpora. The authors describe a hybrid system that combines a search engine and a natural language feature extraction and classification system to estimate the annual incidence of suicide attempts and demonstrate an association of adverse childhood experiences with suicide attempt risk in a cohort of 250,000 patients. The methodology replicated a previous finding that a positive association between suicide attempt incidence and a history of childhood abuse, neglect or family dysfunction exists, and that the association is stronger when multiple adverse events are reported.
Keywords :
behavioural sciences; classification; data mining; diseases; electronic health records; feature extraction; natural language processing; search engines; text analysis; adverse childhood experiences; childhood abuse; classification system; disease; electronic health records; family dysfunction; hybrid text mining approach; multiple adverse events; natural language feature extraction; search engine; suicidal behavior; suicide attempt risk; Feature extraction; History; Natural language processing; Pediatrics; Search engines; Sociology; Statistics; Natural Language Processing; Suicide; Text Search; Veterans;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
System Sciences (HICSS), 2014 47th Hawaii International Conference on
Conference_Location :
Waikoloa, HI
Type :
conf
DOI :
10.1109/HICSS.2014.321
Filename :
6758921
Link To Document :
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