Application of a Hybrid Text Mining Approach to the Study of Suicidal Behavior in a Large Population

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.