THE RELIABLE DATA SYSTEM
AISym4Med aims at developing a platform that will provide healthcare data engineers, practitioners, and researchers access to a trustworthy dataset system augmented with controlled data synthesis for experimentation and modeling purposes. This platform will address data privacy and security by combining new anonymization techniques, attribute-based privacy measures, and trustworthy tracking systems.
THE PLATFORM
This platform will exploit federated technologies for reproducing unidentifiable data from closed borders, promoting the indirect assessment of a broader number of databases, while respecting privacy, security, and GDPR requirements.
The proposed framework will support the development of innovative unbiased AI-based and distributed digital solutions for the benefit of researchers, patients, and providers of health services while maintaining a high level of data privacy and ethical usage. AISym4Med will help in the creation of more robust machine learning (ML) algorithms for real-world readiness. This platform will be validated against local, national, and cross-border use cases for data engineers, ML developers, and aid for clinicians’ operations.
MAIN FEATURES
Access to a trustworthy dataset system augmented with controlled data synthesis for analysis, experimentation, and modeling purposes
New anonymization techniques, attribute-based privacy measures, and trustworthy tracking systems
Data quality controlling measures, including contrast of data biases and respect for ethical norms, context-aware search, and human-centered design for validation purposes
Augmentation module, to explore and develop further the techniques of creating synthetic data, also dynamically on demand for specific use cases
Federated technologies for reproducing unidentifiable data from closed borders, while respecting privacy, security, and GDPR requirements
Development space for innovative unbiased AI-based and distributed tools, technologies, and digital solutions, maintaining a high level of data privacy and ethical usage
Machine-learning meta-engine to provide information on the quality of the generalized model, analyse limits and breaking points, and create a more robust system by supplying on-demand real and/or synthetic data
Dedicated functionalities to facilitate the inspection and improvement of ML models, helping it to be more prepared for the real-world scenarios
Model Auditor functionality, which will enable the analysis and characterization of previously trained models, including model performance, biases identification, limitations, and GDPR compliance
Data Synthesizer module, providing a set of tools for addressing the model issues identified in the auditing step through a data-driven approach
TARGET GROUPS
