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RESULTS AND CHALLENGES OF ARTIFICIAL NEURAL NETWORKS USED FOR DECISION-MAKING AND CONTROL IN MEDICAL APPLICATIONS

Facta Universitatis. Series: Mechanical Engineering, 2019-12, Vol.17 (3), p.285-308

2019. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the associated terms available at http://casopisi.junis.ni.ac.rs/index.php/FUMechEng/about/editorialPolicies#openAccessPolicy. ;ISSN: 0354-2025 ;EISSN: 2335-0164 ;DOI: 10.22190/FUME190327035A

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  • Title:
    RESULTS AND CHALLENGES OF ARTIFICIAL NEURAL NETWORKS USED FOR DECISION-MAKING AND CONTROL IN MEDICAL APPLICATIONS
  • Author: Albu, Adriana ; Precup, Radu-Emil ; Teban, Teodor-Adrian
  • Subjects: Artificial intelligence ; Artificial neural networks ; Decision making ; Iterative methods ; Learning theory ; Modelling ; Neural networks ; Process controls
  • Is Part Of: Facta Universitatis. Series: Mechanical Engineering, 2019-12, Vol.17 (3), p.285-308
  • Description: The aim of this paper is to present several approaches by which technology can assist medical decision-making. This is an essential, but also a difficult activity, which implies a large number of medical and technical aspects. But, more important, it involves humans: on the one hand, the patient, who has a medical problem and who requires the best solution; on the other hand, the physician, who should be able to provide, in any circumstances, a decision or a prediction regarding the current and the future medical status of the patient. The technology, in general, and particularly the Artificial Intelligence (AI) tools could help both of them, and it is assisted by appropriate theory regarding modeling tools. One of the most powerful mechanisms that can be used in this field is the Artificial Neural Networks (ANNs). This paper presents some of the results obtained by the Process Control group of the Politehnica University Timisoara, Romania, in the field of ANNs applied to modeling, prediction and decision-making related to medical systems. An Iterative Learning Control-based approach to batch training a feedforward ANN architecture is given. The paper includes authors’ concerns in this domain and emphasizes that these intelligent models, even if they are artificial, are able to make decisions, being useful tools for prevention, early detection and personalized healthcare.
  • Publisher: Nis: University of Nis
  • Language: English
  • Identifier: ISSN: 0354-2025
    EISSN: 2335-0164
    DOI: 10.22190/FUME190327035A
  • Source: ProQuest Central
    DOAJ Directory of Open Access Journals
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