The development of reliable and effective algorithms depends strongly on the availability, quality, and interpretability of essential data. From biomedical research to healthcare and digital health applications, the transformation of raw data into actionable knowledge requires innovative strategies for data collection, curation, and integration. Novel approaches in machine learning, artificial intelligence, and computational modeling aim to leverage these data to build algorithms that can support precision diagnostics, prognostics, and therapeutic decision-making.
Ensuring data representativeness and reducing bias are key challenges, particularly when dealing with heterogeneous, multimodal, or large-scale datasets. Strategies for secure data sharing, standardization, and validation are crucial to enhance reproducibility and translation into clinical practice.
The symposium will explore the latest strategies to transform essential data into effective algorithms, highlighting innovations, emerging trends, and remaining challenges for practical adoption in real-world healthcare scenarios.
The symposium topic is related to the project D3-4HEALTH (Digital Driven Diagnostics, prognostics, and therapeutics for sustainable Health care) in the frame of PNRR-PNC.