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Machine Learning-Based Clinical Decision Support System for Suicide Risk Management: The PERMANENS Project
Angela Leis1,2, Philippe Mortier1,3, Franco Amigo1,3
1Hospital del Mar Research Institute, Barcelona, Spain.
Studies in Health Technology and Informatics
|May 17, 2025
Summary
The PERMANENS project uses machine learning to aid emergency departments in suicide prevention. It harmonizes data from European registries to support personalized patient care and improve public health outcomes.
Area of Science:
- Public Health
- Artificial Intelligence
- Data Science
Background:
- Suicide and self-harm pose a significant global public health challenge, with over 700,000 deaths annually.
- Effective suicide prevention strategies are crucial, particularly in Europe, which lacks consistent national surveillance systems.
- Emergency departments are critical points for intervention in suicide prevention efforts.
Purpose of the Study:
- To develop a machine learning-based Clinical Decision Support System (CDSS) for emergency departments.
- To enhance personalized care for individuals at risk of self-harm and suicide.
- To create a harmonized European database for real-time analysis of suicide attempt data.
Main Methods:
- Harmonization of national suicide attempt registries from Spain, Ireland, Norway, and Sweden.
- Utilization of the OMOP Common Data Model (CDM) for data standardization.
- Development of a machine learning-based Clinical Decision Support System (CDSS).
Main Results:
- Creation of a comprehensive, harmonized database for real-time analysis of suicide attempt data across multiple European regions.
- Establishment of a framework for utilizing machine learning to support clinical decision-making in emergency departments for suicide prevention.
- Facilitation of personalized care strategies for patients presenting with self-harm or suicidal ideation.
Conclusions:
- The PERMANENS project demonstrates a novel approach to suicide prevention by integrating machine learning and data harmonization.
- The developed CDSS has the potential to significantly improve the capacity of emergency departments to identify and manage individuals at risk.
- Cross-European data collaboration using standardized models is essential for advancing public health surveillance and intervention in suicide prevention.
