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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.Īssessing uncertainty in mechanistic modelsĮdwin J. However, to facilitate truly personalized medicine, new perspectives may be required. Current analytical approaches have included mechanistic models and machine learning (ML), which are often treated as orthogonal viewpoints. Many disease processes are extremely complex and characterized by multiple stochastic processes interacting simultaneously. PMID:29719711īridging paradigms: hybrid mechanistic-discriminative predictive models.ĭoyle, Orla M Tsaneva-Atansaova, Krasimira Harte, James Tiffin, Paul A Tino, Peter DÃaz-Zuccarini, Vanessa A detailed protocol to access quantitative and predictive MLR models is provided as a guide for model development and parameter analysis. Several reports demonstrating the effectiveness of this methodological approach towards reaction optimization and mechanistic interrogation are discussed. Multivariate Linear Regression (MLR) models utilizing computationally-derived and empirically-derived physical organic molecular descriptors are described in this review. Predictive and mechanistic multivariate linear regression models for reaction development