• DocumentCode
    268702
  • Title

    Bridging Paradigms: Hybrid Mechanistic-Discriminative Predictive Models

  • Author

    Doyle, Orla M. ; Tsaneva-Atansaova, K. ; Harte, J. ; Tiffin, P.A. ; Tino, Peter ; Díaz-Zuccarini, Vanessa

  • Author_Institution
    Dept. of Neuroimaging, Inst. of Psychiatry, London, UK
  • Volume
    60
  • Issue
    3
  • fYear
    2013
  • fDate
    Mar-13
  • Firstpage
    735
  • Lastpage
    742
  • Abstract
    Many disease processes are extremely complex and characterized by multiple stochastic processes interacting simultaneously. Current analytical approaches have included mechanistic models and machine learning (ML), which are often treated as orthogonal viewpoints. However, to facilitate truly personalized medicine, new perspectives may be required. This paper reviews the use of both mechanistic models and ML in healthcare as well as emerging hybrid methods, which are an exciting and promising approach for biologically based, yet data-driven advanced intelligent systems.
  • Keywords
    diseases; health care; learning (artificial intelligence); medical computing; stochastic processes; data-driven advanced intelligent systems; disease process; health care; hybrid mechanistic-discriminative predictive models; machine learning; mechanistic models; multiple stochastic process; orthogonal viewpoints; personalized medicine; Biological system modeling; Data models; Diseases; Genetics; Machine learning; Mathematical model; Predictive models; Generative embedding; machine learning (ML); mechanistic models; personalized medicine; Animals; Artificial Intelligence; Biomedical Research; Chronic Disease; Evidence-Based Medicine; Humans; Individualized Medicine; Models, Biological;
  • fLanguage
    English
  • Journal_Title
    Biomedical Engineering, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0018-9294
  • Type

    jour

  • DOI
    10.1109/TBME.2013.2244598
  • Filename
    6449296