Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Modeling of human glucose-insulin kinetics is a complex non-linear multivariate problem. Models are required to represent the dynamics of the human glucose-insulin system and account for multiple edge cases with clinically accepted levels of accuracy. State of the art systems typically incorporate data from clinical trials of large cohorts in order to produce model parameters. Their ability to produce highly accurate results on a per-cohort basis have encouraged researchers to explore the possibility of extending their utility to individual patients. Among these research efforts, machine learning techniques such as support vector machines have been employed with varying levels of success.  proposes a novel adaptive method of generating patient-specific model parameters using multilayer perceptrons. A clinically accepted physiological model of glucose-insulin kinetics is used to generate features that are passed to a multilayer perceptron. Comparison of the predicted versus actual parameters of the model indicate that the MLP method is highly accurate and can be utilized to calibrate physiological models to specific patients.
['Ari J. G. Ramdial', 'Zeljko Zilic']
I am writing a research paper on a idea Adaptive parametric tuning of glucose-insulin kinetics models using multilayer perceptrons
[{"from": "human", "value": "I am writing a research paper on a idea Adaptive parametric tuning of glucose-insulin kinetics models using multilayer perceptrons"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Modeling of human glucose-insulin kinetics is a complex non-linear multivariate problem. Models are required to represent the dynamics of the human glucose-insulin system and account for multiple edge cases with clinically accepted levels of accuracy. State of the art systems typically incorporate data from clinical trials of large cohorts in order to produce model parameters. Their ability to produce highly accurate results on a per-cohort basis have encouraged researchers to explore the possibility of extending their utility to individual patients. Among these research efforts, machine learning techniques such as support vector machines have been employed with varying levels of success.  proposes a novel adaptive method of generating patient-specific model parameters using multilayer perceptrons. A clinically accepted physiological model of glucose-insulin kinetics is used to generate features that are passed to a multilayer perceptron. Comparison of the predicted versus actual parameters of the model indicate that the MLP method is highly accurate and can be utilized to calibrate physiological models to specific patients."}]