New algorithms for data fusion and real-time monitoring of people's health status for eB2 MindCare.
For eB2 MindCare, information collection, data quality, and the internal treatment process are vital. As digitalization has advanced, data has become more distributed and fragmented. Additionally, data generation is faster over time and requires constant updates.
Integrating artificial intelligence into a mental health monitoring system is crucial for improving data quality and, consequently, the system’s effectiveness at each stage of its value chain. By doing so, it enhances data capture, processing, and analysis, boosts personalized predictions and early detection, and ensures a more efficient experience for users (patients, therapists, and family members). Furthermore, AI can reduce bias, automate processes, and provide valuable insights that aid in the continuous improvement of the system, resulting in a more precise, timely, and scalable approach to mental health care through eB2 MindCare.
This project improves the data capture process and automates it, which will lead to a better quality of treated data and, therefore, a higher quality of indicators and predictions obtained from them.
FUNDING
Call from the Ministry of Digitalization of the Community of Madrid: "Development of artificial intelligence use cases applied to industry." This is part of the "Program of territorial networks for technological specialization within the framework of Component 16, Reform 1 of the Recovery, Transformation, and Resilience Plan, financed by the European Union - NextGenerationEU."



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