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Connection ScienceVolume 35, Issue 1, 2023, Article number 2194581

Quality medical data management within an open AI architecture–cancer patients case(Article)(Open Access)

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  • aDepartment of Mathematics and Informatics, University of Novi Sad, Faculty of Sciences, Novi Sad, Serbia
  • bGerman Research Center for Artificial Intelligence (DFKI), Bremen, Germany
  • cNETCOMPANY-INTRASOFT S.A., Luxembourg, Luxembourg

Abstract

In contemporary society people constantly are facing situations that influence appearance of serious diseases. For the development of intelligent decision support systems and services in medical and health domains, it is necessary to collect huge amount of patients’ complex data. Patient’s multimodal data must be properly prepared for intelligent processing and obtained results should be presented in a friendly way to the physicians/caregivers to recommend tailored actions that will improve patients’ quality of life. Advanced artificial intelligence approaches like machine/deep learning, federated learning, explainable artificial intelligence open new paths for more quality use of medical and health data in future. In this paper, we will focus on presentation of a part of a novel Open AI Architecture for cancer patients that is devoted to intelligent medical data management. Essential activities are data collection, proper design and preparation of data to be used for training machine learning predictive models. Another key aspect is oriented towards intelligent interpretation and visualisation of results about patient’s quality of life obtained from machine learning models. The Architecture has been developed as a part of complex project in which 15 institutions from 8 European countries have been participated. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

Author keywords

cancer patientsCloud/Edge distributed environmentdata management in medical domainsQuality of life

Indexed keywords

Engineering controlled terms:ArchitectureDecision support systemsDiseasesMachine learning
Engineering uncontrolled termsCancer patientsCloud/edge distributed environmentData management in medical domainDecision support servicesDistributed environmentsIntelligent decision-support systemsMedical data managementMedical domainsPatient caseQuality of life
Engineering main heading:Information management

Funding details

Funding sponsor Funding number Acronym
875351
  • 1

    This research was supported by the ASCAPE project. The ASCAPE project has received funding from the European Union’s Horizon 2020 Research and Innovation Framework Programme under grant agreement No. 875351.

  • ISSN: 09540091
  • Source Type: Journal
  • Original language: English
  • DOI: 10.1080/09540091.2023.2194581
  • Document Type: Article
  • Publisher: Taylor and Francis Ltd.

  Ivanovic, M.; Department of Mathematics and Informatics, University of Novi Sad, Faculty of Sciences, Trg Dositeja Obradovica 4, Novi Sad, Serbia;
© Copyright 2023 Elsevier B.V., All rights reserved.

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