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Nursing-care data in this paper are Japanese texts written by nurses which consist of answers for questions about nursing-care. The nursing-care data are collected via WWW application from many hospitals in Japan. The collected data are stored into the database. The nursing-care experts evaluate the collected data to improve nursing-care quality. Currently, the collected data are evaluated by experts reading all texts carefully. It is difficult, however, for experts to evaluate the data because there are huge number of nursing-care data in the database. In this paper, to reduce workloads for the evaluation of nursing-care data, neural networks are used for classifying nursing-care data instead of fuzzy classification system. We use standard three-layer feedforward neural networks with back-propagation type learning. First, we extract attribute values (i.e., training data) from texts written by nurses. And then, we train a neural network using the training data. From computer simulations, we show the effectiveness of our proposed system using the leaving-one out method.
Nii et al. (Tue,) studied this question.