Intelligent Management of Multimodal Health Data for Decision-Making in Big Data Scenarios - IHealth-MD

• Grant number: 24/13328-9
• Support Opportunities: Research Projects - Thematic Grants
• Start date: August 01, 2025
• End date: July 31, 2030
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
• Principal Investigator: Agma Juci Machado Traina
• Grantee: Agma Juci Machado Traina
• Host Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
• Pesquisadores principais: Caetano Traina Junior ; José Eduardo Krieger ; José Fernando Rodrigues Júnior ; Marco Antonio Gutierrez
• Associated researchers: Abdelsalam Helal ; Christos Faloutsos ; Cláudio Douglas Gouveia Linhares ; Diego Armando Cardona Cardenas ; Elaine Parros Machado de Sousa ; Estela Ribeiro ; Guilherme Sydow Nunes Bueno Brandão ; Jean Roberto Ponciano ; Joe Tekli ; Jorge Elias Júnior ; Marcel Koenigkam Santos ; Marcello Henrique Nogueira Barbosa ; Marcos Vinicius Naves Bêdo ; Marina de Fátima de Sá Rebelo ; Mirela Teixeira Cazzolato ; Paolo Soda ; Paulo Mazzoncini de Azevedo Marques ; Ramon Alfredo Moreno ; Richard Chbeir ; Rosa Sicilia ; Rudinei Goularte ; Sameer Antani ; Sebastian Bickelhaupt

Abstract

The proposal focuses on the challenges of managing and integrating multimodal health data for knowledge discovery, aiming at extracting meaningful information from large volumes of complex and diverse health data by leveraging Big Data and Artificial Intelligence (AI) for the Health Sciences. Almost every human activity now generate and require storing and processing vast quantities of diverse and complex data, from scientific, academic, and business to leisure activities. Health-related activities are no different, as they produce big data and can benefit from technological advancements to enhance decision-making processes through the information extracted from this data.In a clinical environment, electronic health records (EHRs) are the foundation for developing information extraction strategies. In this proposal, we intend to develop and integrate novel and scalable algorithms powered by data engineering (DE) and AI methods. These algorithms will leverage large amounts of EHR and clinical data repositories to gather valuable and significant information for decision-making. Data communication and security aspects will firmly be considered, ensuring the integrity and privacy of the data while enabling efficient knowledge discovery.The size and complexity of EHR databases, which include structured and unstructured text, signals, images, lab results, and genomic data, present significant challenges for processing. These challenges encompass the application of analysis techniques and the development of practical tools and subsequent applications. However, these databases also present numerous opportunities to develop new algorithms and methods capable of displaying smart and relevant information related to individual patients or groups of patients. This can transform EHRs into more effective platforms, enhancing support for healthcare professionals, optimizing medical applications, and informing strategic government decisions in line with the demands and benefits of big data. In this project, we aim to address the challenges of managing and integrating not only the information but also the knowledge from multiple modalities of health data, focusing initially on lung-related diseases, primarily lung cancer, as well as cardiovascular diseases. We will develop methods and algorithms that will ultimately be materialized in a modular platform, which will be made available to the healthcare community. (AU)