Bridging paradigms: hybrid mechanistic-discriminative predictive models
OM Doyle, K Tsaneva-Atansaova… - IEEE Transactions …, 2013 - ieeexplore.ieee.org
IEEE Transactions on Biomedical Engineering, 2013•ieeexplore.ieee.org
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 …
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 …
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.
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