Prediction of suicidal ideation and depression in the general population with subthreshold insomnia using machine learning models
This study leveraged machine learning models to indirectly screen for suicidal ideation (SI) and moderate-to-severe depression in a Slovenian nationwide community sample, specifically focusing on individuals with subthreshold insomnia. The models, developed using socio-demographics, life satisfaction, behavioral changes, and coping strategies, demonstrated robust and consistent predictive performance across both insomnia and non-insomnia groups. The findings highlight the potential for these ML models as feasible, ethical, and efficient tools for early detection in high-risk populations, particularly those presenting with sleep complaints, potentially reducing preventable morbidity and mortality.
Executive Impact: Key Performance Indicators
The ML models demonstrated strong and consistent predictive performance, showcasing their robustness and potential for broad applicability in clinical settings.
0.78
AUROC for SI Model (Insomnia Group)
0.80
AUROC for SI Model (Non-Insomnia Group)
0.79
AUROC for Depression Model (Insomnia Group)
0.82
AUROC for Depression Model (Non-Insomnia Group)
10 min
Minutes for Administration
Deep Analysis & Enterprise Applications
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Enterprise Process Flow
Data Acquisition (Slovenian Community Sample)
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Data Preprocessing & Feature Engineering (Socio-demographics, Life Satisfaction, Behavior, COPE Strategies)
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Model Training (Logistic Regression for SI & Depression)
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Validation (Stratified by Insomnia Severity Index)
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Performance Evaluation (AUROC)
0.78-0.82
Consistent AUROC Across All Groups and Models
| Feature | Traditional Screening | ML-based Indirect Screening |
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| Ethical Considerations |
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Early Intervention in High-Risk Populations
The models offer a critical opportunity for timely intervention in a high-risk population. Given that sleep complaints are a common reason for seeking healthcare, this approach provides a feasible and ethical pathway for early detection of suicidal ideation and depression.
Impact: Integrating these ML models into primary care or digital health platforms can significantly reduce preventable morbidity and mortality associated with suicide and depression by enabling targeted interventions before conditions escalate.
Calculate Your Potential AI Impact
Estimate the hours reclaimed and cost savings your enterprise could achieve by automating tasks related to risk assessment and early detection using AI.
Annual Cost Savings
$0
Hours Reclaimed Annually
0
Your AI Implementation Roadmap
A structured approach to integrating ML models for early detection and risk assessment into your enterprise, ensuring ethical, efficient, and impactful deployment.
Phase 1: Pilot Deployment & Clinical Integration
Integrate ML models into existing clinical workflows in pilot healthcare settings. Conduct user acceptance testing and gather feedback from clinicians and patients.
Phase 2: External Validation & Generalizability Study
Validate model performance in diverse cultural contexts and varying public health circumstances to assess generalizability and robustness.
Phase 3: Digital Health Platform Integration
Develop APIs and integrations for seamless deployment into digital health applications, online screening tools, and electronic health records.
Phase 4: Longitudinal Monitoring & Refinement
Implement continuous monitoring of model performance and patient outcomes. Incorporate new data and advanced features (e.g., biological markers, digital phenotyping) for iterative refinement.
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