Summary of Iasis: Towards Heterogeneous Big Data Analysis For Personalized Medicine, by Anastasia Krithara et al.

iASiS: Towards Heterogeneous Big Data Analysis for Personalized Medicine

by Anastasia Krithara, Fotis Aisopos, Vassiliki Rentoumi, Anastasios Nentidis, Konstantinos Bougatiotis, Maria-Esther Vidal, Ernestina Menasalvas, Alejandro Rodriguez-Gonzalez, Eleftherios G. Samaras, Peter Garrard, Maria Torrente, Mariano Provencio Pulla, Nikos Dimakopoulos, Rui Mauricio, Jordi Rambla De Argila, Gian Gaetano Tartaglia, George Paliouras

First submitted to arxiv on: 9 Jul 2024

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Summary difficulty	Written by	Summary
High	Paper authors	High Difficulty Summary Read the original abstract here
Medium	GrooveSquid.com (original content)	Medium Difficulty Summary The IASIS project aims to transform large amounts of biomedical data into actionable knowledge for decision-makers by integrating disparate sources such as genomics, electronic health records, and bibliography. The integration is facilitated through advanced analytics methods that discover useful patterns, enabling personalized diagnosis and treatment. The iASiS infrastructure converts clinical notes into usable data, combining them with genomic data, images, and more to create a global knowledge base. This enables the use of intelligent methods to discover patterns across different resources. Semantic integration provides rich, auditable, and reliable information for better care, reduced errors, and increased confidence in sharing data.
Low	GrooveSquid.com (original content)	Low Difficulty Summary The IASIS project takes lots of medical data from different places and helps make it useful for people who make decisions about healthcare. It combines different types of data, like genetic information and doctor’s notes, to find patterns that can help doctors diagnose and treat patients better. The project also makes sure the data is accurate and reliable, which makes it easier for people to share and use. Two examples of how this works are with dementia and lung cancer.