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Identifying suicide ideation in mental health application posts: A random forest algorithm.

The growing use of digitized mental health applications requires new reliable early screening tools to identify user suicide risk. We used a lexicon-based random forest machine learning algorithm to predict suicide ideation scores from 714 online community text posts from December 2019 to April 2020...

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Publicado en:Death Studies Vol. 47; no. 9; pp. 1044 - 1053
Autores principales: Moradian, Hoora, Lau, Mark A., Miki, Andrew, Klonsky, E. David, Chapman, Alexander L.
Formato: Artículo
Publicado: Taylor & Francis Ltd 2023
Materias:
Social media
Mental health
Medical screening
Suicidal ideation
Digital health
Random forest algorithms
Machine learning
Risk assessment
Descriptive statistics
Research funding
Algorithms
Early diagnosis
Acceso en línea:Ver este registro en EBSCOhost
Sumario:The growing use of digitized mental health applications requires new reliable early screening tools to identify user suicide risk. We used a lexicon-based random forest machine learning algorithm to predict suicide ideation scores from 714 online community text posts from December 2019 to April 2020. We validated predicted scores against expert-rated suicide ideation scores. The algorithm-predicted scores offered high validity and a low error rate and correctly identified 95% of expert-rated high-risk suicide ideation posts. Our findings highlight a potential new method to detect suicidal ideation of digital mental health application users.