Reiko Sakashita

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.

Preventing Aspiration Pneumonia Among the Elderly: A Review Focused on the Impact of the Consistency of Food Substances

Aspiration pneumonia is the leading cause of death among the elderly. Modified-texture foods, i.e., foods with altered consistency, are recommended in order to maintain both normal swallowing and adequate nutrition, which is also expected to reduce aspiration pneumonia, when elderly people are suspected to suffer from disorders of eating and/or swallowing. However, it is reported that overly-restrictive diets have been provided to most residents given modified-texture diets. Furthermore, there is scant empirical evidence of the medical effectiveness of food texture-modification. Little attention has been paid to the effect of the consistency of food substances, as well as the ability of mastication, on general health. Our cross-sectional studies showed that eaters of regular foods have lower incidences of pneumonia and fever, while those eating modified-texture, i.e., softer and finer, foods have higher incidences of pneumonia and fever. In this review, the effects of interventions for prevention of aspiration pneumonia were overviewed then the impact of the consistency of food substances on the health of the elderly and the direction of further research was discussed.

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.

An Emerging Integrated Middle-Range Theory on Asian Women’s Leadership in Nursing:

Introduction: Asian cultures reflect patriarchal cultural values and attitudes, which likely have influenced women leaders in their countries differently from women in Western cultures. However, virtually no leadership theories have been developed to reflect the experiences and development of nursing leaders from Asian cultures. The purpose of this article is to present an emerging integrated middle-range theory on Asian women’s leadership in nursing. Methodology: Using an integrative approach, the theory was developed based on three major sources: the leadership frames of Bolman and Deal, literature reviews, and exemplars/cases from five different countries. Results: The theory includes two main domains (leadership frames and leadership contexts). The domain of leadership frames includes human resources/networks, structure/organization, national/international politics, and symbols. The domain of leadership contexts includes cultural contexts, sociopolitical contexts, and gendered contexts. Discussion: This theory will help understand nursing leadership in Asian cultures and provide directions for future nurse leaders in this ever-changing globalized world.

Distance learning for maternal and child health nurses and midwives in Mongolia: a qualitative evaluation

BACKGROUND Continuing education is vital for the development of the competencies of nurses and midwives. We analysed the effectiveness of a distance education programme for maternal and child health nurses and midwives in Mongolia, assessing its strengths and limitations and ways in which it could be improved. The aim of this research is to provide an analysis of the successes and failures of the programme, in order to improve future versions of this and similar programmes in Mongolia and elsewhere. METHODS We carried out a qualitative descriptive study in Mongolia in August 2015. This consisted of three semi-structured interviews and two focus groups in the Second National Hospital, Ulaanbaatar, and three semi-structured interviews and one focus group in Dornogovi Provincial Maternal Hospital, Sainshand, Dornogovi Province. In total, there were 22 participants in our research. Data from the interviews and focus groups were thematically coded and analysed using NVivo version 10. FINDINGS The distance education programme is well received by participants. They suggest that it has improved their clinical practice and education in a number of areas, and are anxious for the programme to continue. A number of alterations would be necessary to improve both the quality of the programme and the ability of participants to foster change on the basis of what they have learnt. This provides challenges for both the programme organizers and the providers of maternal and child health services in Mongolia. IMPLICATIONS FOR NURSING AND/OR HEALTH POLICY The success of the distance education programme suggests that collaborations of this type are a cost-effective method of disseminating best practice in policy and practice to improve the quality of care provided to mothers and children in low-resource settings. CONCLUSIONS A distance education programme is vital to link maternal care providers in Mongolia to new trends in care. Mongolias relative isolation means that this programme is particularly valuable there. However, the programme could work equally well in other developing country settings.

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.

Impact of the consistency of food substances on the health of residents in welfare facilities for seniors

The effect of the consistency of food substances on health has not been assessed. The ingestion of food substances which require mastication promotes salivary secretion and oral function, which may in turn lead to a lower incidence of aspiration pneumonia. Thus, this study aims to reveal the importance of ingesting regular solid food and inquires into the condition associated with ingesting regular food.