Abstract: A method and apparatus for the distributed programmable control of multiple devices, each capable of independent operation and of performing a set of related functions or operations, to operate concurrently or sequentially and in cooperation to perform a predetermined task or operation. A device controller is associated with each process device wherein each device controller controls the operations of the associated process device and includes a process step memory for a storing a device process. Each device process includes one or more device steps wherein each device step corresponds to a process step and controls one or more corresponding operations of the associated process device.

Abstract: Neural networks are constructed (programmed), trained on historical data, and used to predict any of (1) optimal patient dosage of a single drug, (2) optimal patient dosage of one drug in respect of the patient's concurrent usage of another drug, (3a) optimal patient drug dosage in respect of diverse patient characteristics, (3b) sensitivity of recommended patient drug dosage to the patient characteristics, (4a) expected outcome versus patient drug dosage, (4b) sensitivity of the expected outcome to variant drug dosage(s), (5) expected outcome(s) from drug dosage(s) other than the projected optimal dosage. Both human and economic costs of both optimal and sub-optimal drug therapies may be extrapolated from the exercise of various optimized and trained neural networks. Heretofore little recognized sensitivities—such as, for example, patient race in the administration of psychotropic drugs—are made manifest.