A Controlled Trial Using Natural Language Processing to Examine the Language of Suicidal Adolescents in the Emergency Department |
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| Authors: | John P. Pestian PhD Jacqueline Grupp‐Phelan MD Kevin Bretonnel Cohen PhD Gabriel Meyers LSW Linda A. Richey LSW Pawel Matykiewicz MS Michael T. Sorter MD |
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| Affiliation: | 1. Department of Pediatrics, Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center and Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, OH, USA;2. Division of Emergency Medicine, Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA;3. Computational Bioscience Program, University of Colorado School of Medicine, Denver, CO, USA;4. Division of Psychiatry, Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA |
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| Abstract: | 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. |
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