Atsuko Uchinuno

Nursing-care Data Classification using Neural Networks

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.

Nursing-Care Freestyle Text Classification Using Support Vector Machines

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 experts 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 workloads evaluating nursing-care data, we propose a support vector machine(SVM) based classification system.

New feature definition for improvement of Nursing-care text classification

Recently, “Web based Nursing-care Quality Improvement System” have been proposed and operating continuously for improving the nursing-care quality in Japan. For evaluating actual nursing-care process, freestyle Japanese texts which are called “nursing-care texts” are collected through the Internet. The nursing-care experts read the collected nursing-care texts carefully to evaluate actual nursing-care process. Then they make a recommendation which includes some improvements, and send it to each nurse. The number of nursing-care experts who can evaluate the nursing-care texts is a few. Hence, it is hard to perform the above mentioned evaluation process because of a large number of nurses. In order to assist nursing-care experts in evaluating the nursing-care texts, we have been developing a computer aided nursing-care text classification system. In this paper, first, we introduce our computer aided nursing-care text classification system. Then we propose a method to improve the classification performance of the nursing-care text classification system. In our proposed method, dependency relation between terms is extracted from the nursing-care text. The extracted dependency is used as a feature value which represents characteristics of each nursing-care text. From some experimental results for the actual nursing-care text sets, we show that our proposed feature definition is effective for improving the classification performance.

Fuzzy Rule Extraction from Nursing-Care Texts

The nursing care quality improvement is very important for our life. Currently, nursing-care freestyle texts (nursing-care data) are collected from many hospitals in Japan by using Web applications. The collected nursing-care data are stored into the database. To evaluate nursing-care data, we have already proposed a fuzzy classification system, a neural network based system, a support vector machine (SVM) based classification system. Then, in order to improve the classification performance, we have proposed a genetic algorithm (GA) based feature selection method for generating numerical data from collected nursing-care texts.In this paper, we propose a fuzzy rule extraction method from the nursing-care text data. First, features of nursing-care texts are selected by a genetic algorithm based feature selection method. Next, numerical training data are generated by using selected features. Then we train neural networks using generated training data. Finally, fuzzy if-then rules are extracted from the trained neural networks by the parallelized rule extraction method.From computer simulation results, we show the effectiveness of our proposed method.

Nursing-care text classification using additional term information from Web

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.

Nursing-care text classification using word vector representation and convolutional neural networks

In this paper, we propose a convolutional neural network (CNN) based classification method for nursing-care classification. CNNs have obtained strong performance in computer vision speech recognition areas. Recently, CNNs have been also applied sentence classification. We have studied nursing-care text classification [6]-[18]. In our former works, we proposed several types of feature definitions and examined some classification models. In this paper, each text is represented as a concatenated word vector. Then, every text is classified using CNN-based classification methods. We examined some classification models at the classification layer in CNNs. From our experimental results, the proposed CNN-based method obtained better performance than our former works.

Dependency Relation Based Feature Vectors for Classifying Nursing-Care Texts

It is very important for us to improve the nursing-care quality. To improve the nursing-care quality, the “Web based Nursing-care Quality Improvement System” have been proposed and operating contin...

Genetic Algorithm Based Term Selection for Nursing-Care Freestyle Japanese Text Classification

Abstract In this paper, we propose two term selection methods for classifying nursing-care texts. In a term selection method based on GA, two objectives which are maximizing correctly classified texts and minimizing selected terms are optimized. The weighted sum of these two objectives was used as the evaluation function. Therefore, GA-based term selection is performed aiming at the improvement in classification performance on testing sets. In a NSGA-II based term selection method, non-dominated solutions are found. As the result, we can have a set of pareto-optimal solutions. These solutions are helpful to analyze classification results from the viewpoint of terms. From experimental results, we show effectiveness of our proposed term selection methods.