Prediction of Repeated Self-Harm in Six Months
Comparison of Traditional Psychometrics With Random Forest Algorithm
Bibliographic Data
| ID | 13627904 |
|---|---|
| Authors | Shu‐Chin Chen (0000-0003-3316-2164, National Taiwan University), Shu-Chin Chen (National Taiwan University), Hui‐Chun Huang (0000-0002-9695-0777, Mackay Junior College of Medicine, Nursing and Management), Hui-Chun Huang (Department of Medical Research, Mackay Memorial Hospital, Taipei, Taiwan), Shen-Ing Liu (Department of Psychiatry, Mackay Memorial Hospital, Taipei, Taiwan), Shen‐Ing Liu (Mackay Memorial Hospital, corresponding author), Sue‐Huei Chen (0000-0001-6737-2966, National Taiwan University, corresponding author), Sue-Huei Chen (National Taiwan University) |
| Year | 2021 |
| Volume | 88 |
| Issue | 4 |
| Pages | 1403-1429 |
| Publication date | 2021-12-17 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | OMEGA - Journal of Death and Dying (JOURNAL) |
| Journal identifiers | ISSN: 0030-2228 • E-ISSN: 1541-3764 |
| Publisher | SAGE Publishing (PUBLISHER • US) |
| DOI | 10.1177/00302228211060596 |
| PMID | 34920680 |
| OpenAlex | W4200166366 |
| Language | EN |
| Citations received | 1 |
| References cited | 44 |
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
Algorithm · Harm · Logistic regression · Machine learning · Random forest · Scale (ratio · Statistics · Artificial Intelligence · Clinical Psychology · Computer Science · Insect and Pesticide Research · Mathematics · Medicine · Mental Health Treatment and Access · Psychology · Social Psychology · Suicide and Self-Harm Studies
The Suicidal Behaviors Questionnaire-Revised (SBQ-R)
Machine learning
Towards an Integrated Motivational–Volitional Model of Suicidal Behaviour
Suicide and suicidal behaviour
A simulation study of the number of events per variable in logistic regression analysis
Suicide, Suicide Attempts, and Suicidal Ideation
Classification of Suicide Attempts through a Machine Learning Algorithm Based on Multiple Systemic Psychiatric Scales
A Feasibility Study Using a Machine Learning Suicide Risk Prediction Model Based on Open-Ended Interview Language in Adolescent Therapy Sessions
Risk factors for suicidal thoughts and behaviors
| Citations per year | 1 |
|---|---|
| Citation span | 2025 - 2025 (1) |
| Highly cited | No |