Suicide Note Classification Using Natural Language Processing: A Content Analysis.
Suicide is the second leading cause of death among 25-34 year olds and the third leading cause of death among 15-25 year olds in the United States. In the Emergency Department, where suicidal patients often present, estimating the risk of repeated attempts is generally left to clinical judgment. Thi...
| Publicado en: | Biomedical Informatics Insights Vol. 3; pp. 19 - 29 |
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| Autores principales: | , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
2010
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104896830&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104896830 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 11782226 B077 jtl: Biomedical Informatics Insights issn: 11782226 maglogo: Y pubinfo: dt: 2010 vid: 3 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 104896830 60596097 10.4137/BII.S4706 104896830 ppf: 19 ppct: 10 formats: fmt: @attributes: type: P tig: atl: Suicide Note Classification Using Natural Language Processing: A Content Analysis. aug: au: Pestian, John Nasrallah, Henry Matykiewicz, Pawel Bennett, Aurora Leenaars, Antoon affil: Department of Biomedical Informatics, Cincinnati Children's Hospital Medical Center sug: subj: Suicide Natural Language Processing Forensic Medicine Human Algorithms Adult Middle Age White Persons Male Readability Emotions Wilcoxon Rank Sum Test Funding Source Adult: 19-44 years Middle Aged: 45-64 years Male ab: Suicide is the second leading cause of death among 25-34 year olds and the third leading cause of death among 15-25 year olds in the United States. In the Emergency Department, where suicidal patients often present, estimating the risk of repeated attempts is generally left to clinical judgment. This paper presents our second attempt to determine the role of computational algorithms in understanding a suicidal patient's thoughts, as represented by suicide notes. We focus on developing methods of natural language processing that distinguish between genuine and elicited suicide notes. We hypothesize that machine learning algorithms can categorize suicide notes as well as mental health professionals and psychiatric physician trainees do. The data used are comprised of suicide notes from 33 suicide completers and matched to 33 elicited notes from healthy control group members. Eleven mental health professionals and 31 psychiatric trainees were asked to decide if a note was genuine or elicited. Their decisions were compared to nine different machine-learning algorithms. The results indicate that trainees accurately classified notes 49% of the time, mental health professionals accurately classified notes 63% of the time, and the best machine learning algorithm accurately classified the notes 78% of the time. This is an important step in developing an evidence-based predictor of repeated suicide attempts because it shows that natural language processing can aid in distinguishing between classes of suicidal notes. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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