Premium
A Controlled Trial Using Natural Language Processing to Examine the Language of Suicidal Adolescents in the Emergency Department
Author(s) -
Pestian John P.,
GruppPhelan Jacqueline,
Bretonnel Cohen Kevin,
Meyers Gabriel,
Richey Linda A.,
Matykiewicz Pawel,
Sorter Michael T.
Publication year - 2016
Publication title -
suicide and life‐threatening behavior
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.544
H-Index - 90
eISSN - 1943-278X
pISSN - 0363-0234
DOI - 10.1111/sltb.12180
Subject(s) - emergency department , suicide prevention , conversation , poison control , injury prevention , human factors and ergonomics , gesture , occupational safety and health , psychology , medical prescription , test (biology) , medical emergency , medicine , psychiatry , clinical psychology , artificial intelligence , computer science , nursing , communication , paleontology , pathology , biology
What adolescents say when they think about or attempt suicide influences the medical care they receive. Mental health professionals use teenagers' words, actions, and gestures to gain insight into their emotional state and to prescribe what they believe to be optimal care. This prescription is often inconsistent among caregivers, however, and leads to varying outcomes. This variation could be reduced by applying machine learning as an aid in clinical decision support. We designed a prospective clinical trial to test the hypothesis that machine learning methods can discriminate between the conversation of suicidal and nonsuicidal individuals. Using semisupervised machine learning methods, the conversations of 30 suicidal adolescents and 30 matched controls were recorded and analyzed. The results show that the machines accurately distinguished between suicidal and nonsuicidal teenagers.