title: Bridging paradigms: hybrid mechanistic-discriminative predictive models.
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.
authors: Department of Neuroimaging, Institute of Psychiatry, King's College London, London, UK. orla.doyle@kcl.ac.uk
authors: Orla M
authors: OM
authors: Doyle
authors: Krasimira
authors: K
authors: Tsaneva-Atansaova
authors: James
authors: J
authors: Harte
authors: Paul A
authors: PA
authors: Tiffin
authors: Peter
authors: P
authors: Tino
authors: Vanessa
authors: V
authors: Díaz-Zuccarini
mesh_terms: D000818:Animals; D001185:Artificial Intelligence; D035843:Biomedical Research; D002908:Chronic Disease; D019317:Evidence-Based Medicine; D006801:Humans; D008954:Models, Biological; D057285:Precision Medicine