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A DUAL NEURAL NETWORK FOR TREATMENT RECOMMENDATIONS

A course of medical treatment attacks a target (such as a tumor) with one or more agents. The action of the agents on the human body is complex, both with respect to the interaction of the medications with one another, and with respect to secondary effects on systems unrelated to the target. A system consisting of two neural networks is presented for optimizing the effect(s) on an individual. The technique is borrowed from neural network control systems. One network is trained to predict the outcome of treatment, and the other netowk optimizes the predicted outcome. The system is tested with a variety of types of artificially generated data. Results give a proof of the concept and indicate how the system can be adjusted to optimize combinations of multiple symptoms (primary and side effects) using multiple interacting agents.