Hiroki Moriguchi

Predictability of the future development of aggressive behavior of cranial dural arteriovenous fistulas based on decision tree analysis.

OBJECT The severity of clinical signs and symptoms of cranial dural arteriovenous fistulas (DAVFs) are well correlated with their pattern of venous drainage. Although the presence of cortical venous drainage can be considered a potential predictor of aggressive DAVF behaviors, such as intracranial hemorrhage or progressive neurological deficits due to venous congestion, accurate statistical analyses are currently not available. Using a decision tree data mining method, the authors aimed at clarifying the predictability of the future development of aggressive behaviors of DAVF and at identifying the main causative factors. METHODS Of 266 DAVF patients, 89 were eligible for analysis. Under observational management, 51 patients presented with intracranial hemorrhage/infarction during the follow-up period. RESULTS The authors created a decision tree able to assess the risk for the development of aggressive DAVF behavior. Evaluated by 10-fold cross-validation, the decision trees accuracy, sensitivity, and specificity were 85.28%, 88.33%, and 80.83%, respectively. The tree shows that the main factor in symptomatic patients was the presence of cortical venous drainage. In its absence, the lesion location determined the risk of a DAVF developing aggressive behavior. CONCLUSIONS Decision tree analysis accurately predicts the future development of aggressive DAVF behavior.

Role of CD13/Aminopeptidase N in Rat Lymphocytic Alveolitis Caused by Thoracic Irradiation

Abstract Huang, L., Tani, K., Ogushi, F., Ogawa, H., Shimizu, T., Motoki, Y., Moriguchi, H. and Sone, S. Role of CD13/Aminopeptidase N in Rat Lymphocytic Alveolitis Caused by Thoracic Irradiation. Radiat. Res. 157, 191–198 (2002). CD13/aminopeptidase N is a cell surface glycoprotein that is widely distributed in a variety of mammalian cells. It was recently shown to have chemotactic activity for T lymphocytes. This study examined the role of CD13/aminopeptidase N in lymphocytic alveolitis in radiation-induced lung injury caused by a single-dose thoracic irradiation (15 Gy) in rats. Significantly increased aminopeptidase activity was detected in bronchoalveolar lavage fluid obtained from irradiated rats at 4 weeks after irradiation compared to the activity in unirradiated rats. Significantly higher aminopeptidase activity was detected on alveolar macrophages from irradiated rats at 2 and 4 weeks than on those from unirradiated rats. Western blot analysis showed an increased expression of CD13/aminopeptidase N protein in alveolar macrophages from irradiated rats at 4 weeks. Chemotactic activity for normal rat lymphocytes was detected in bronchoalveolar lavage fluid from irradiated rats at 4 weeks, and approximately 60% of the activity was inhibited by pretreatment of bronchoalveolar lavage fluid with bestatin, a specific aminopeptidase inhibitor. This study suggests that CD13/aminopeptidase N may play an important role as a lymphocyte chemoattractant in lymphocyte-mediated alveolitis in experimental radiation-induced lung injury.

On-Admission Pressure Ulcer Prediction Using the Nursing Needs Score

Background Pressure ulcers (PUs) are considered a serious problem in nursing care and require preventive measures. Many risk assessment methods are currently being used, but most require the collection of data not available on admission. Although nurses assess the Nursing Needs Score (NNS) on a daily basis in Japanese acute care hospitals, these data are primarily used to standardize the cost of nursing care in the public insurance system for appropriate nurse staffing, and have never been used for PU risk assessment. Objective The objective of this study was to predict the risk of PU development using only data available on admission, including the on-admission NNS score. Methods Logistic regression was used to generate a prediction model for the risk of developing PUs after admission. A random undersampling procedure was used to overcome the problem of imbalanced data. Results A combination of gender, age, surgical duration, and on-admission total NNS score (NNS group B; NNS-B) was the best predictor with an average sensitivity, specificity, and area under receiver operating characteristic curve (AUC) of 69.2% (6920/100), 82.8% (8280/100), and 84.0% (8400/100), respectively. The model with the median AUC achieved 80% (4/5) sensitivity, 81.3% (669/823) specificity, and 84.3% AUC. Conclusions We developed a model for predicting PU development using gender, age, surgical duration, and on-admission total NNS-B score. These results can be used to improve the efficiency of nurses and reduce the number of PU cases by identifying patients who require further examination.

Systemic inflammation and family history in relation to the prevalence of type 2 diabetes based on an alternating decision tree

To investigate unknown patterns associated with type 2 diabetes in the Japanese population, we first used an alternating decision tree (ADTree) algorithm, a powerful classification algorithm from data mining, for the data from 1,102 subjects aged 35–69 years. On the basis of the investigated patterns, we then evaluated the associations of serum high-sensitivity C-reactive protein (hs-CRP) as a biomarker of systemic inflammation and family history of diabetes (negative, positive or unknown) with the prevalence of type 2 diabetes because their detailed associations have been scarcely reported. Elevated serum hs-CRP levels were proportionally associated with the increased prevalence of type 2 diabetes after adjusting for probable covariates, including body mass index and family history of diabetes (P for trend = 0.016). Stratified analyses revealed that elevated serum hs-CRP levels were proportionally associated with increased prevalence of diabetes in subjects without a family history of diabetes (P for trend = 0.020) but not in those with a family history or with an unknown family history of diabetes. Our study demonstrates that systemic inflammation was proportionally associated with increased prevalence of type 2 diabetes even after adjusting for body mass index, especially in subjects without a family history of diabetes.

Mining Nurse Care Data: A Study Case on Pressure Ulcer Prediction

The possibility of extracting useful medical information from data collected by nurses for management purposes is investigated. An alternating decision tree for predicting pressure ulcer development is generated from nursing needs score data (NNS) usually recorded in Japanese hospitals.

Empirical Study of Sampling Methods for Classification in Imbalanced Clinical Datasets

Many clinical data suffer from data imbalance in which we have large number of instances of one class and small number of instances of the other. This problem affects most machine learning algorithms especially decision trees. In this study, we investigated different undersampling and oversampling algorithms applied to multiple imbalanced clinical datasets. We evaluated the performance of decision tree classifiers built for each combination of dataset and sampling method. We reported our experiment results and found that the considered oversampling methods generally outperform undersampling ones using AUC performance measure.

Supporting self-control of individual training for motor-skill development with a social web environment

This study proposes a system to support self-controlled motor-skill development in a web-community environment. We discuss the difficulties involved in sustaining self-controlled training without systematic supports. The system proposed provides a function to suggest an appropriate range of target goals based on data from previous training sessions. A prototype system has been designed and developed. This study reports a case study based on a trial use of the system. The results suggest that our approach contributes to each users ability to achieve target goals.

Supporting Development of Motor-Skills in a Comfortable Environment

This paper describes a technical approach for supporting motor-skill development of human beings. We focus on the strong relationship between physical performance and mind status. Therefore, our approach integrates the brain wave-tool to monitor at the time of exercising. Then the system automatically selects appropriate media as for the feedback for improving mind status.