TY - JOUR
T1 - Bridging paradigms
T2 - Hybrid mechanistic-discriminative predictive models
AU - Doyle, Orla M.
AU - Tsaneva-Atansaova, Krasimira
AU - Harte, James
AU - Tiffin, Paul A.
AU - Tino, Peter
AU - Diáz-Zuccarini, Vanessa
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
KW - Generative embedding
KW - Machine learning (ML)
KW - Mechanistic models
KW - Personalized medicine
UR - http://www.scopus.com/inward/record.url?scp=84884610985&partnerID=8YFLogxK
U2 - 10.1109/TBME.2013.2244598
DO - 10.1109/TBME.2013.2244598
M3 - Review article
C2 - 23392334
AN - SCOPUS:84884610985
SN - 0018-9294
VL - 60
SP - 735
EP - 742
JO - IEEE Transactions on Biomedical Engineering
JF - IEEE Transactions on Biomedical Engineering
IS - 3
ER -