Treatment optimization with a neural control system

Typical medical diagnosis applications of neural networks for prediction and classification require training data (observations) that include the "correct" category for a number of patient records. In this paper, we borrow a technique from control systems applications of neural networks. Optimal control parameters of a system are typically not known. Instead, we only know the effect on a remote system. The correct control action drives the remote system optimally. The learning technique requires two networks: one to model the system to be controlled (here, the patient), and one to optimize the treatment (here, the treating physician). The concept was tested with artificially generated noisy data, and gives promising results.