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ASCAPE - An Intelligent Approach to Support Cancer Patients

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Information Systems and Technologies (WorldCIST 2023)

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.

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Notes

  1. 1.

    https://doi.org/10.1016/S1470-2045(16)00091-7.

  2. 2.

    https://ascape-project.eu/.

  3. 3.

    https://ascape-project.eu/the_project/consortium.

References

  1. 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

  2. ASCAPE Deliverable - D2.4 ML-DL Training and Evaluation Report (2022). https://ascape-project.eu/node/118

  3. ASCAPE Deliverable - D4.1 Personalized interventions and user-centric visualizations (2022). https://ascape-project.eu/node/120

  4. ASCAPE framework and technical innovations (2022). https://ascape-project.eu/marketing-material/ascape-framework-and-technical-innovations

  5. 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)

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  6. Kairouz, P., et al.: Advances and open problems in federated learning. Found. Trends Mach. Learn.14(1-2), 1–210 (2021)

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  7. 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)

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  8. 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

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Acknowledgment

This research was supported by the EU H2020 ASCAPE project under grant agreement No 875351.

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Correspondence to Mirjana Ivanović .

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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

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