Prediction of Repeated Self-Harm in Six Months: Comparison of Traditional Psychometrics With Random Forest Algorithm.
Suicidal risk has been a significant mental health problem. However, the predictive ability for repeated self-harm (SH) has not improved over the past decades. This study thus aimed to explore a potential tool with theoretical accommodation and clinical application by employing traditional logistic...
| Publicado en: | Omega: Journal of Death & Dying Vol. 88; no. 4; pp. 1403 - 1430 |
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| Autores principales: | , , , |
| Formato: | Artículo |
| Publicado: |
Sage Publications Inc.
Mar2024
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| Materias: | |
| 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=175231396&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 175231396 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00302228 OME jtl: Omega: Journal of Death & Dying issn: 00302228 maglogo: Y pubinfo: dt: Mar2024 vid: 88 iid: 4 pid: 344 pub: Sage Publications Inc. artinfo: ui: 175231396 10.1177/00302228211060596 ppf: 1403 ppct: 27 formats: tig: atl: Prediction of Repeated Self-Harm in Six Months: Comparison of Traditional Psychometrics With Random Forest Algorithm. aug: au: Chen, Shu-Chin Huang, Hui-Chun Liu, Shen-Ing Chen, Sue-Huei affil: Department of Psychology, 33561 National Taiwan University, Taipei, Taiwan Suicide Prevention Center, 36897 MacKay Memorial Hospital, Taipei, Taiwan Department of Medical Research, 36897 MacKay Memorial Hospital, Taipei, Taiwan 63360 MacKay Junior College of Medicine, Nursing and Management, Taipei, Taiwan Department of Psychiatry, 36897 MacKay Memorial Hospital, Taipei, Taiwan su: Suicide risk factors Psychometrics Self-mutilation Confidence intervals Random forest algorithms Machine learning Risk assessment Comparative studies Descriptive statistics Prediction models Logistic regression analysis Algorithms sug: subj: Suicide risk factors Psychometrics Self-mutilation Confidence intervals Random forest algorithms Machine learning Risk assessment Comparative studies Descriptive statistics Prediction models Logistic regression analysis Algorithms keyword: machine learning random forest self-harm self-injury suicide machine learning random forest self-harm self-injury suicide ab: Suicidal risk has been a significant mental health problem. However, the predictive ability for repeated self-harm (SH) has not improved over the past decades. This study thus aimed to explore a potential tool with theoretical accommodation and clinical application by employing traditional logistic regression (LR) and newly developed machine learning, random forest algorithm (RF). Starting with 89 items from six commonly used scales (i.e., proximal suicide risk factors) as preliminary predictors, both LR and RF resulted in a better solution with much fewer items in two phases of item selections and analyses, with prediction accuracy 88.6% and 79.8%, respectively. A combination with 12 selected items, named LR-12, well predicted repeated self-harm in 6-month follow-up with satisfactory performance (AUC = 0.84, 95% CI: 0.76–0.92; cut-off point by 1/2 with sensitivity 81.1% and specificity 74.0%). The psychometrically appealing LR-12 could be used as a screening scale for suicide risk assessment. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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