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
Link To Document :
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