Feature extraction from nursing-care texts for classification

The nursing care quality improvement is very important in the medical field. Currently, nursing-care freestyle texts (nursing-care data) are collected from many hospitals in Japan by using Web applications and stored into the database. Some nursing-care experts evaluate the collected data to improve nursing care quality. For evaluating the nursing-care data, experts need to read all freestyle texts carefully and then classified them into four classes. However, it is a very hard task for each expert to evaluate the data because of huge number of nursing-care data in the database. In order to reduce workloads evaluating nursing-care data, we have proposed a support vector machine (SVM) based classification system. In this paper, to improve the classification performance, we propose a feature extraction method for generating numerical data from collected nursing-care texts. In our proposed method, the frequency in use of a term in the term list is used for selecting features which contribute to the classification. And then, the nursing-care numerical data are classified by the SVM based classification system. From computer simulation results, we show the effectiveness of our proposed method.