期刊
HEALTH INFORMATICS JOURNAL
卷 25, 期 4, 页码 1846-1862出版社
SAGE PUBLICATIONS INC
DOI: 10.1177/1460458218796598
关键词
electronic health records; natural language processing; psychiatric stressor extraction; statistical association; suicide behavior
资金
- National Library of Medicine [2R01LM 010681-05]
Suicide takes the lives of nearly a million people each year and it is a tremendous economic burden globally. One important type of suicide risk factor is psychiatric stress. Prior studies mainly use survey data to investigate the association between suicide and stressors. Very few studies have investigated stressor data in electronic health records, mostly due to the data being recorded in narrative text. This study takes the initiative to automatically extract and classify psychiatric stressors from clinical text using natural language processing-based methods. Suicidal behaviors were also identified by keywords. Then, a statistical association analysis between suicide ideations/attempts and stressors extracted from a clinical corpus is conducted. Experimental results show that our natural language processing method could recognize stressor entities with an F-measure of 89.01percent. Mentions of suicidal behaviors were identified with an F-measure of 97.3percent. The top three significant stressors associated with suicide are health, pressure, and death, which are similar to previous studies. This study demonstrates the feasibility of using natural language processing approaches to unlock information from psychiatric notes in electronic health record, to facilitate large-scale studies about associations between suicide and psychiatric stressors.
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