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...
| Publicado en: | Death Studies Vol. 47; no. 9; pp. 1044 - 1053 |
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| Autores principales: | , , , , |
| Formato: | Artículo |
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Taylor & Francis Ltd
2023
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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=ssf&AN=164942602&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 164942602 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 07481187 DEA jtl: Death Studies issn: 07481187 maglogo: N pubinfo: dt: 2023 vid: 47 iid: 9 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 164942602 10.1080/07481187.2022.2160519 ppf: 1044 ppct: 9 formats: tig: atl: Identifying suicide ideation in mental health application posts: A random forest algorithm. aug: au: Moradian, Hoora Lau, Mark A. Miki, Andrew Klonsky, E. David Chapman, Alexander L. affil: Starling Minds, Vancouver, British Columbia, Canada Department of Psychiatry, University of British Columbia, Vancouver, Canada Department of Psychology, University of British Columbia, Vancouver, Canada Department of Psychology, Simon Fraser University, Burnaby, Canada su: Social media Mental health Medical screening Suicidal ideation Digital health Random forest algorithms Machine learning Risk assessment Descriptive statistics Research funding Algorithms Early diagnosis sug: subj: Social media Mental health Medical screening Suicidal ideation All Other Miscellaneous Ambulatory Health Care Services Offices of Mental Health Practitioners (except Physicians) Digital health Random forest algorithms Machine learning Risk assessment Descriptive statistics Research funding Algorithms Early diagnosis ab: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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