Artificial neural network based ‘continuous feedback loop’ platform to support multicenter cardiac clinical trials

Multicenter clinical trials are complex that traditional rigid concepts of data acquisition and analysis algorithms may culminate in poor trial design, incomplete protocols, unidentified variables and absence of feedback modification. This provides no flexibility to accommodate new, useful and informative predictors during the course of the trial. Thus most of the trials will realize the limitations by the end of the study period. This fact motivated the development of a suite of decision support tools utilizing an artificial neural network (ANN) based platform. We conceptualize an ANN based platform for supporting large clinical trials based on a continuous feedback loop mechanism to enable restructuring the data acquisition, data analysis and may identify novel hidden clinical correlations and findings thus significantly improving the efficiency of the trial outcomes.