Development of a nursing diagnosis system using a back-propagation neural network model: an application for stomach cancer patients.
Neural networks have recently attracted considerable attention in the field of classification and in other areas. This paper discusses an experiment using a back-propagation neural network model applied to nursing diagnosis. The network's structure has three layers: 1) an input layer for representing signs and symptoms, 2) an output layer for nursing diagnosis, 3) a hidden layer. A first prototype of a nursing diagnosis system for stomach cancer patients was developed with 87 nodes for the input layer and 5 nodes for the output layer of 23 nursing diagnoses. It turned out that a model that learned 10,000 times as fast and had 5 nodes of hidden layers, a 0.5 momentum, and a 0.5 learning coefficient is the optimal one with a hitting ratio 0.76.
Park
H A
HA
College of Nursing, Seoul National University, 28 Yongon-dong, Chongno-gu, Seoul, 110-799 Korea.
Lee
E O
EO
Song
M S
MS
D003198:Computer Simulation; D003936:Diagnosis, Computer-Assisted; D005246:Feedback; D006801:Humans; D008954:Models, Biological; D016571:Neural Networks, Computer; D009732:Nursing Care; D012984:Software; D013274:Stomach Neoplasms / Q000175:diagnosis / Q000451:nursing*