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Lecture Notes in Networks and SystemsVolume 799 LNNS, 2024, Pages 271-27711th World Conference on Information Systems and Technologies, WorldCIST 2023; Pisa; Italy; 4 April 2023 through 6 April 2023; Code 308049

ASCAPE - An Intelligent Approach to Support Cancer Patients(Conference Paper)

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  • University of Novi Sad, Faculty of Sciences, Novi Sad, Serbia

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. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Author keywords

Federated LearningMachine LearningPatients’ Quality of LifePersonalized Services in Medicine

Indexed keywords

Engineering controlled terms:DiseasesIntelligent systemsMachine learning
Engineering uncontrolled termsCancer patientsFederated learningMachine-learningMulti-disciplinary researchNumber of peoplesPatient dataPatient’ quality of lifePersonalized servicePersonalized service in medicineQuality of life
Engineering main heading:Hospitals
  • ISSN: 23673370
  • ISBN: 978-303145641-1
  • Source Type: Book Series
  • Original language: English
  • DOI: 10.1007/978-3-031-45642-8_27
  • Document Type: Conference Paper
  • Volume Editors: Rocha A.,Adeli H.,Dzemyda G.,Moreira F.,Colla V.
  • Publisher: Springer Science and Business Media Deutschland GmbH

  Ivanović, M.; University of Novi Sad, Faculty of Sciences, Novi Sad, Serbia;
© Copyright 2024 Elsevier B.V., All rights reserved.

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