Patent ID: 6968327

Claim:
A method for training a neural network in order to identify a patient risk function such that the structure of the neural network is simplified, wherein the neural network includes an input layer having a plurality of input neurons that receive input data, at least one intermediate layer having a plurality of intermediate neurons, an output layer having a plurality of output neurons that provide output signals, wherein the output signals define the patient risk function following a first occurrence of a disease on the basis of given training data records including objectifiable and metrologically captured data relating to the medical condition of a patient, and a multiplicity of synapses, wherein each said synapse interconnects a first neuron of a first layer with a second neuron of a second layer, defining a data sending and processing direction from the input layer toward the output layer, wherein the method comprises: identifying and eliminating synapses of the multiplicity of synapses that have an influence on the curve of the risk function that is less than a predetermined significance including determining pre-change output signals of the neural network, selecting first and second sending neurons that are connected to the same receiving neuron by respective first and second synapses, assuming a correlation of response signals from said first and second sending neurons to the same receiving neuron, interrupting the first synapse and adapting in its place the weight of the second synapse, determining post-change output signals of the neural network, comparing the post-change output signals with the pre-change output signals, and eliminating the first synapse if the comparison result does not exceed a predetermined level.