Nonlinear System Identification Based on Convolutional Neural Networks for Multiple Drug Interactions.

In heart failure patients, hemodynamics can be regulated by therapeutic drugs. Although the cardiovascular responses to these drugs usually include nonlinearity and drug interactions, it is difficult to identify the characteristics of the dynamics under such conditions. This study, therefore, was aimed at evaluating the technique used for nonlinear system identification based on convolutional neural networks (CNN). As an image i.e., pixel values corresponding to time-course data), CNN can be used to treat the complicated relation between previous inputs (i.e., drug infusions) and outputs i.e., hemodynamics). To compare the accuracy of CNN, traditional methods based on the standard neural networks (NN) and fast Fourier transformation FFT were applied to nonlinear system identification with drug interactions. The cardiac output and arterial blood pressure under heart failure were modulated by the drug infusions of an inotropic agent and a vasodilator. CNN accurately predicted the dynamic system responses regardless of the inclusion of nonlinearity and drug interactions. Based on the findings of this study, CNN to carry out nonlinear system identification could clarify complicated pharmacodynamics, and thus could be useful for in appropriate cardiac treatment with multiple therapeutic agents.