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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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SHTI-294-SHTI220601.pdf | Published version | 271.23 kB | Adobe PDF | View/Open |
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