Abstract
Background
Predicting intentional self-harm recidivism is critical for reducing preventable mortality and optimizing resource allocation in mental healthcare. While existing risk assessment tools rely on clinician-reported factors, machine learning offers data-driven insights into underrecognized predictors.
Methods
We developed an XGBoost gradient boosting model using 7,375 records from the EU-IDB Full Dataset (41.7% with intentional self-harm recidivism), employing SHAP (SHapley Additive exPlanations) analysis to identify key predictors. The model was optimized via dynamic threshold adjustment (optimal threshold: 0.3479). SHAP values are shown for each feature.
Results
The model demonstrated excellent accuracy: precision 0.78, recall 0.78 and an F1-score of 0.78, with an AUC 0.86. SHAP analysis revealed critical drivers of recurrence: Mechanism of injury (0.58), Activity when injured (0.33), and Object involved (0.29). Among the most impactful modalities by feature were: Mechanism of injury (foreign body 1.01, exposure to chemical or other substance 0.84); Activity when injured (other specified leisure or play 0.47, unspecified leisure or play 0.43); Object involved (personal grooming utensil 0.81 e.g. razor, razor blade and sedatives or antipsychotics 0.53). Although Age showed a moderate overall importance (0.20), individual-level analysis highlighted a subset of patients aged 46-62 years with significantly elevated SHAP values (up to 0.77). Temporal features such as the COVID-19 period had limited impact (0.06), indicating that pandemic effects were secondary to clinical and situational factors.
Conclusions
The model’s high accuracy (AUC 86%) supports its potential as a screening tool in emergency departments, a prioritization system for follow-up care, and a template for prevention programs. Future research should validate these findings across diverse healthcare systems.
Key messages
• Machine learning model predicts self-harm recidivism with high accuracy identifying key risk factors.
• Key predictors are mechanism of injury, activity during injury, and objects involved, more than demographic factors.
Topic
Self-harm injury, Risk prediction, SHAP analysis.
Predicting intentional self-harm recidivism is critical for reducing preventable mortality and optimizing resource allocation in mental healthcare. While existing risk assessment tools rely on clinician-reported factors, machine learning offers data-driven insights into underrecognized predictors.
Methods
We developed an XGBoost gradient boosting model using 7,375 records from the EU-IDB Full Dataset (41.7% with intentional self-harm recidivism), employing SHAP (SHapley Additive exPlanations) analysis to identify key predictors. The model was optimized via dynamic threshold adjustment (optimal threshold: 0.3479). SHAP values are shown for each feature.
Results
The model demonstrated excellent accuracy: precision 0.78, recall 0.78 and an F1-score of 0.78, with an AUC 0.86. SHAP analysis revealed critical drivers of recurrence: Mechanism of injury (0.58), Activity when injured (0.33), and Object involved (0.29). Among the most impactful modalities by feature were: Mechanism of injury (foreign body 1.01, exposure to chemical or other substance 0.84); Activity when injured (other specified leisure or play 0.47, unspecified leisure or play 0.43); Object involved (personal grooming utensil 0.81 e.g. razor, razor blade and sedatives or antipsychotics 0.53). Although Age showed a moderate overall importance (0.20), individual-level analysis highlighted a subset of patients aged 46-62 years with significantly elevated SHAP values (up to 0.77). Temporal features such as the COVID-19 period had limited impact (0.06), indicating that pandemic effects were secondary to clinical and situational factors.
Conclusions
The model’s high accuracy (AUC 86%) supports its potential as a screening tool in emergency departments, a prioritization system for follow-up care, and a template for prevention programs. Future research should validate these findings across diverse healthcare systems.
Key messages
• Machine learning model predicts self-harm recidivism with high accuracy identifying key risk factors.
• Key predictors are mechanism of injury, activity during injury, and objects involved, more than demographic factors.
Topic
Self-harm injury, Risk prediction, SHAP analysis.
| Original language | English |
|---|---|
| Number of pages | 1 |
| Publication status | Published - 14 Nov 2025 |
| Event | 12th European Conference on Injury Prevention and Safety Promotion (EU-SAFETY 2025) - Heraklion, Greece Duration: 30 Sept 2025 → 2 Oct 2025 https://academic.oup.com/eurpub/issue/35/Supplement_5?trk=feed_main-feed-card_feed-article-content&login=false |
Conference
| Conference | 12th European Conference on Injury Prevention and Safety Promotion (EU-SAFETY 2025) |
|---|---|
| Abbreviated title | EU-SAFETY 2025 |
| Country/Territory | Greece |
| City | Heraklion |
| Period | 30/09/25 → 2/10/25 |
| Internet address |
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