About the Project

Today, nearly every human activity generates and requires the storage and processing of vast amounts of diverse and complex data across scientific, academic, business, and leisure domains. Health-related activities are no exception, producing large-scale data that can benefit from technological advancements to enhance decision-making, increasingly driven by insights derived from this data. In a clinical environment, electronic health records (EHRs) are the cornerstone for developing information extraction strategies. This proposal aims to design and integrate novel, scalable algorithms supported by databases and artificial intelligence techniques to harness the potential of large EHR datasets and clinical data repositories, yielding valuable insights for decision-making. Data communication via federated learning will also be addressed to ensure privacy-preserving and security.

The size and complexity of EHR databases offer significant challenges when they need to be processed in terms of applying analysis techniques and supporting the development of subsequent applications for practical tools. However, it also embodies many opportunities to create algorithms and methods able to display smart and relevant information related to either a particular patient or groups of patients and to boost the EHR into a more effective platform to support the healthcare professionals, coping medical applications and strategic government decisions with the demands and benefits of Big Data. In this project, we aim to deal with the challenges of managing and integrating not only the information, but also the knowledge from multiple modalities of health data. We will develop methods and algorithms that will ultimately be materialized in a modular platform to be made available to the area community.