Abstract
Nowadays the number of people living with cancer is constantly increasing. Numerous multidisciplinary research teams are working on development of powerful intelligent systems that will support medical decisions and help patients with critical diseases, including cancer, to keep and even increase their quality of life (QoL). ASCAPE (Artificial intelligence Supporting CAncer Patients across Europe) is an H2020 project which main objective is to use powerful techniques in Big Data, Artificial Intelligence and Machine Learning in processing cancer (breast and prostate) patients’ data in order to support their health status. A key result of the project is the implementation of an Artificial Intelligence/Machine Learning (AI/ML) infrastructure. It will allow the deployment and execution of AI/ML algorithms locally in a hospital on patients’ private data, producing new knowledge. Newly generated knowledge will be sent back to the infrastructure and will be available to other users of the system keeping private patients’ data locally in hospitals. In this paper we will briefly present the structure of an open AI/ML infrastructure and how federated learning is employed in it.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
ASCAPE Deliverable - D1.1 Positioning ASCAPE’s open Al infrasetructure in the after cancer-care Iron Triangle of Health (2022). https://ascapeproject.eu/node/57
ASCAPE Deliverable - D2.4 ML-DL Training and Evaluation Report (2022). https://ascape-project.eu/node/118
ASCAPE Deliverable - D4.1 Personalized interventions and user-centric visualizations (2022). https://ascape-project.eu/node/120
ASCAPE framework and technical innovations (2022). https://ascape-project.eu/marketing-material/ascape-framework-and-technical-innovations
Holzinger, A., Langs, G., Denk, H., Zatloukal, K., Müller, H.: Causability and explainability of artificial intelligence in medicine. Wiley Interdisciplinary Rev. Data Mining Knowl. Discovery 9(4), e1312 (2019)
Kairouz, P., et al.: Advances and open problems in federated learning. Found. Trends Mach. Learn.14(1-2), 1–210 (2021)
Lampropoulos, K., et al.: ASCAPE: an open AI ecosystem to support the quality of life of cancer patients. In: 2021 IEEE 9th International Conference on Healthcare Informatics (ICHI), pp. 301–310. IEEE (2021)
Savić, M., et al.: The application of machine learning techniques in prediction of quality of life features for cancer patients. Comput. Sci. Inf. Syst. 20(1), 381–404 (2023). https://doi.org/10.2298/CSIS220227061S
Acknowledgment
This research was supported by the EU H2020 ASCAPE project under grant agreement No 875351.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Ilić, M. et al. (2024). ASCAPE - An Intelligent Approach to Support Cancer Patients. In: Rocha, A., Adeli, H., Dzemyda, G., Moreira, F., Colla, V. (eds) Information Systems and Technologies. WorldCIST 2023. Lecture Notes in Networks and Systems, vol 799. Springer, Cham. https://doi.org/10.1007/978-3-031-45642-8_27
Download citation
DOI: https://doi.org/10.1007/978-3-031-45642-8_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-45641-1
Online ISBN: 978-3-031-45642-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)

