Standardized diagnosis and treatment approaches in mental healthcare struggle to contextualize the unique complexities inherent in individuals. There is data pertaining to demand being unstructured and dispersed across various sources, though.

Aim of this WP is to create a federated learning platform based on which a decision-support system is made that enables personalized diagnosis and treatment advice. We develop advanced methodologies, e.g. pattern recognition techniques to discern structures within both demand and supply data. This then leads to establishing comprehensive characterizations and constructing decision trees that enhance our ability to navigate and respond to the complexities inherent in healthcare demand.