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Intelligent Decision Support Systems in Automated Medical Diagnosis

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Advances in Biomedical Informatics

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 137))

Abstract

The Intelligent Decision Support Systems (IDSSs) represent an interdisciplinary research domain bringing together Artificial Intelligence/Machine Learning (AI/ML), Decision Science (DS), and Information Systems (IS). IDSS refers to the use of AI/ML techniques in decision support systems. In this context, it should be emphasized the special role of statistical learning (SL) in the process of training algorithms from data. The purpose of this chapter is to provide a short review of some of the state-of-the-art AI/ML algorithms, seen as intelligent tools used in the medical decision-making, along with some important applications in the automated medical diagnosis of some major chronic diseases (MCDs). In addition, we aim to present an interesting approach to develop novel IDSS inspired by the evolutionary paradigm.

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Author information

Authors and Affiliations

  1. Chair of Mathematics, Biostatistics and Computer Science, University of Medicine and Pharmacy of Craiova, Petru Rares 2-4, 200349, Craiova, Romania

    Florin Gorunescu

  2. Department of Computer Science, Faculty of Sciences, University of Craiova, A.I. Cuza 13, 200585, Craiova, Romania

    Smaranda Belciug

Authors
  1. Florin Gorunescu
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  2. Smaranda Belciug
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Corresponding author

Correspondence to Florin Gorunescu .

Editor information

Editors and Affiliations

  1. Dept. of Statistics & Applied Probabilit, University of California Santa Barbara, Santa Barbara, California, USA

    Dawn E. Holmes

  2. KES International , Adelaide, South Australia, Australia

    Lakhmi C. Jain

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Gorunescu, F., Belciug, S. (2018). Intelligent Decision Support Systems in Automated Medical Diagnosis. In: Holmes, D., Jain, L. (eds) Advances in Biomedical Informatics. Intelligent Systems Reference Library, vol 137. Springer, Cham. https://doi.org/10.1007/978-3-319-67513-8_8

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  • DOI: https://doi.org/10.1007/978-3-319-67513-8_8

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  • Online ISBN: 978-3-319-67513-8

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