Nursing-care text classification using additional term information from Web.
ABSTRACT In this paper, for improving performance of the nursing-care text classification, we introduce a mechanism of retrieving terms from Web. Every year, the nursing-care texts are collected by using Web application to improve nursing-care quality in Japan. The collected nursing-care texts are decomposed into morphemes (i.e., terms), and then terms are stored as a term list. Each text is represented as a feature vector by using the term list and classified using a SVM based classification system. The training data sets for constructing SVM based classification system are different from the evaluation data sets. That is, there are differences between the term lists of the nursing-care texts because the nursing-care texts are collected and evaluated every year. To cover this difference, we introduce a mechanism of retrieving terms from Web. A new term which appeared in the evaluation data sets is used as a query of a search engine. The terms in the term list are also used as queries. Terms are represented by the search results, and then are compared with each other. We use the most similar term in the term list as an alternative of the new term. From experimental results, we show effectiveness of our proposed method.
Conference Proceeding: Nursing-care Data Classification using Neural Networks[show abstract] [hide abstract]
ABSTRACT: 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.Complex Medical Engineering, 2007. CME 2007. IEEE/ICME International Conference on; 06/2007
- JACIII. 01/2010; 14:142-149.
Conference Proceeding: Nursing-Care Freestyle Text Classification Using Support Vector Machines[show abstract] [hide abstract]
ABSTRACT: 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. Some nursing-care ex- perts evaluate the collected data to improve nursing care quality. For evaluating the nursing-care data, experts need to read all freestyle texts carefully. However, it is a hard task for an expert to evaluate the data because of huge number of nursing-care data in the database. In order to reduce work- loads evaluating nursing-care data, we propose a support vector machine(SVM) based classification system.Granular Computing, 2007. GRC 2007. IEEE International Conference on; 12/2007