Development of a sensory-neural network for medical diagnosing
This is an incremental approach for medical professionals to automate disease diagnosis based on symptom indicators.
The paper tackles medical diagnosis by developing a sensory-neural network that uses patient questionnaire responses as sensor signals to excite neurons representing diseases, with the most excited neuron indicating the diagnosis and reliability estimated by a likelihood ratio.
Performance of a sensory-neural network developed for diagnosing of diseases is described. Information about patient's condition is provided by answers to the questionnaire. Questions correspond to sensors generating signals when patients acknowledge symptoms. These signals excite neurons in which characteristics of the diseases are represented by synaptic weights associated with indicators of symptoms. The disease corresponding to the most excited neuron is proposed as the result of diagnosing. Its reliability is estimated by the likelihood defined by the ratio of excitation of the most excited neuron and the complete neural network.