https://scholars.lib.ntu.edu.tw/handle/123456789/606265
標題: | Prediction of Repeated Self-Harm in Six Months: Comparison of Traditional Psychometrics With Random Forest Algorithm | 作者: | Chen S.-C Huang H.-C Liu S.-I SUE-HUEI CHEN |
關鍵字: | machine learning;random forest;self-harm;self-injury;suicide | 公開日期: | 2021 | 來源出版物: | Omega (United States) | 摘要: | 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. ? The Author(s) 2021. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121833689&doi=10.1177%2f00302228211060596&partnerID=40&md5=3f756badf1ed39f349e64dbfc9b19271 https://scholars.lib.ntu.edu.tw/handle/123456789/606265 |
ISSN: | 00302228 | DOI: | 10.1177/00302228211060596 |
顯示於: | 心理學系 |
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