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Bridging Paradigms: Hybrid Mechanistic-Discriminative Predictive Models

Doyle, O.M.; Tsaneva-Atansaova, K.; Harte, J.; Tiffin, P.A.; Tino, P.; Díaz-Zuccarini, V.

Authors

O.M. Doyle

K. Tsaneva-Atansaova

J. Harte

P.A. Tiffin

P. Tino

V. Díaz-Zuccarini



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.

Citation

Doyle, O., Tsaneva-Atansaova, K., Harte, J., Tiffin, P., Tino, P., & Díaz-Zuccarini, V. (2013). Bridging Paradigms: Hybrid Mechanistic-Discriminative Predictive Models. IEEE Transactions on Biomedical Engineering, 60(3), 735-742. https://doi.org/10.1109/tbme.2013.2244598

Journal Article Type Article
Publication Date Mar 11, 2013
Deposit Date Feb 1, 2013
Journal IEEE Transactions on Biomedical Engineering
Print ISSN 0018-9294
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 60
Issue 3
Pages 735-742
DOI https://doi.org/10.1109/tbme.2013.2244598

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