iASiS: Towards Heterogeneous Big Data Analysis for Personalized Medicine

Bibliographic Details
Title: iASiS: Towards Heterogeneous Big Data Analysis for Personalized Medicine
Authors: Krithara, Anastasia, Aisopos, Fotis, Rentoumi, Vassiliki, Nentidis, Anastasios, Bougatiotis, Konstantinos, Vidal, Maria-Esther, Menasalvas, Ernestina, Rodriguez-Gonzalez, Alejandro, Samaras, Eleftherios G., Garrard, Peter, Torrente, Maria, Pulla, Mariano Provencio, Dimakopoulos, Nikos, Mauricio, Rui, De Argila, Jordi Rambla, Tartaglia, Gian Gaetano, Paliouras, George
Source: 2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS), Cordoba, Spain, 2019, pp. 106-111
Publication Year: 2024
Collection: Computer Science
Subject Terms: Computer Science - Artificial Intelligence, Computer Science - Databases
More Details: The vision of IASIS project is to turn the wave of big biomedical data heading our way into actionable knowledge for decision makers. This is achieved by integrating data from disparate sources, including genomics, electronic health records and bibliography, and applying advanced analytics methods to discover useful patterns. The goal is to turn large amounts of available data into actionable information to authorities for planning public health activities and policies. The integration and analysis of these heterogeneous sources of information will enable the best decisions to be made, allowing for diagnosis and treatment to be personalised to each individual. The project offers a common representation schema for the heterogeneous data sources. The iASiS infrastructure is able to convert clinical notes into usable data, combine them with genomic data, related bibliography, image data and more, and create a global knowledge base. This facilitates the use of intelligent methods in order to discover useful patterns across different resources. Using semantic integration of data gives the opportunity to generate information that is rich, auditable and reliable. This information can be used to provide better care, reduce errors and create more confidence in sharing data, thus providing more insights and opportunities. Data resources for two different disease categories are explored within the iASiS use cases, dementia and lung cancer.
Comment: 6 pages, 2 figures, accepted at 2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)
Document Type: Working Paper
DOI: 10.1109/CBMS.2019.00032
Access URL: http://arxiv.org/abs/2407.06748
Accession Number: edsarx.2407.06748
Database: arXiv
More Details
DOI:10.1109/CBMS.2019.00032