Bridging paradigms: Hybrid mechanistic-discriminative predictive models

Orla M. Doyle, Krasimira Tsaneva-Atansaova, James Harte, Paul A. Tiffin, Peter Tino, Vanessa Diáz-Zuccarini

Research output: Contribution to journalReview articlepeer-review

11 Scopus citations

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 M Lin healthcare as well as emerging hybrid methods, which are an exciting and promising approach for biologically based, yet data-driven advanced intelligent systems.

Original languageEnglish
Pages (from-to)735-742
Number of pages8
JournalIEEE Transactions on Biomedical Engineering
Volume60
Issue number3
DOIs
StatePublished - 2013
Externally publishedYes

Keywords

  • Generative embedding
  • Machine learning (ML)
  • Mechanistic models
  • Personalized medicine

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