Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/37834
Title: Augmenting THerapeutic Effectiveness Through Novel Analytics (ATHENA) – A Public and Private Partnership Project Funded by the Flemish Government (VLAIO)
Authors: Petsophonsakul, Ploingarm
PIRMANI, Ashkan 
DE BROUWER, Edward 
Akand, Murat
Botermans, Wouter
Van Der Aa, Frank
Vermeesch, Joris Robert
Offner, Fritz
Wuyts, Roel
Moreau, Yves
Maes, Ingrid
Blockx, Ines
Van Rompuy, Patricia
Lewi, Martine
VANNIEUWENHUYSE, Bart 
Issue Date: 2022
Source: Séroussi, B.; Weber, P.; Dhombres, F.; Grouin, C.; Liebe, J.; Pelayo, S.; Pinna, A.; Rance, B.; Sacchi, L.; Ugon, A.; Benis, A.; Gallos, P. (Eds.). Volume 294: Challenges of Trustable AI and Added-Value on Health, p. 829 -833
Series/Report: Studies in Health Technology and Informatics
Series/Report no.: 294
Abstract: The complexity and heterogeneity of cancers leads to variable responses of patients to treatments and interventions. Developing models that accurately predict patient's care pathways using prognostic and predictive biomarkers is increasingly important in both clinical practice and scientific research. The main objective of the ATHENA project is to: (1) accelerate data driven precision medicine for two use cases-bladder cancer and multiple myeloma, (2) apply distributed and privacy-preserving analytical methods/ algorithms to stratify patients (decision support), (3) help healthcare professionals deliver earlier and better targeted treatments, and (4) explore care pathway automations and improve outcomes for each patient. Challenges associated with data sharing and integration will be addressed and an appropriate federated data ecosystem will be created, enabling an interoperable foundation for data exchange, analysis and interpretation. By combining multidisciplinary expertise and tackling knowledge gaps in ATHENA, we propose a novel federated privacy preserving platform for oncology research.
Keywords: Precision medicine;Federated platform;Machine learning;Distributed analytics;Data science 1 Corresponding Author;Ploingarm Petsophonsakul;
Document URI: http://hdl.handle.net/1942/37834
ISBN: 9781643682846
9781643682853
DOI: 10.3233/shti220601
Rights: 2022 European Federation for Medical Informatics (EFMI) and IOS Press. This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0).
Category: B2
Type: Book Section
Appears in Collections:Research publications

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