• Communications for Statistical Applications and Methods
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• v.15 no.4
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• pp.605-614
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• 2008

Oh (2008) has proposed the one-sided likelihood ratio test of the equality of two agreement measures. However the use of this test may be limited since the computations of test statistic and critical value are not easy. We propose a test for comparing two dependent agreements using some well known tests for marginal homogeneity, for instance, Bhapkar test, Stuart-Maxwell test. Data obtained from 2008 world figure skating championship ladies single is analyzed for illustration purposes.

• Journal of Korean Medical Science
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• v.33 no.52
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• pp.347.1-347.11
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• 2018

Background: Smartphone addiction has recently been highlighted as a major health issue among adolescents. In this study, we assessed the degree of agreement between adolescents' and parents' ratings of adolescents' smartphone addiction. Additionally, we evaluated the psychosocial factors associated with adolescents' and parents' ratings of adolescents' smartphone addiction. Methods: In total, 158 adolescents aged 12-19 years and their parents participated in this study. The adolescents completed the Smartphone Addiction Scale (SAS) and the Isolated Peer Relationship Inventory (IPRI). Their parents also completed the SAS (about their adolescents), SAS-Short Version (SAS-SV; about themselves), Generalized Anxiety Disorder-7 (GAD-7), and Patient Health Questionnaire-9 (PHQ-9). We used the paired t-test, McNemar test, and Pearson's correlation analyses. Results: Percentage of risk users was higher in parents' ratings of adolescents' smartphone addiction than ratings of adolescents themselves. There was disagreement between the SAS and SAS-parent report total scores and subscale scores on positive anticipation, withdrawal, and cyberspace-oriented relationship. SAS scores were positively associated with average minutes of weekday/holiday smartphone use and scores on the IPRI and father's GAD-7 and PHQ-9 scores. Additionally, SAS-parent report scores showed positive associations with average minutes of weekday/holiday smartphone use and each parent's SAS-SV, GAD-7, and PHQ-9 scores. Conclusion: The results suggest that clinicians need to consider both adolescents' and parents' reports when assessing adolescents' smartphone addiction, and be aware of the possibility of under- or overestimation. Our results cannot only be a reference in assessing adolescents' smartphone addiction, but also provide inspiration for future studies.

• Korean Journal of Radiology
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• v.23 no.9
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• pp.866-877
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• 2022

Objective: The optimal imaging approach for evaluating pathological nipple discharge remains unclear. We investigated the value of adding ductography to ultrasound (US) for evaluating pathologic nipple discharge in patients with negative mammography findings. Materials and Methods: From July 2003 to December 2018, 101 women (mean age, 46.3 ± 12.2 years; range, 23-75 years) with pathologic nipple discharge were evaluated using pre-ductography (initial) US, ductography, and post-ductography US. The imaging findings were reviewed retrospectively. The standard reference was surgery (70 patients) or > 2 years of follow-up with US (31 patients). The diagnostic performances of initial US, ductography, and post-ductography US for detecting malignancy were compared using the McNemar's test or a generalized estimating equation. Results: In total, 47 papillomas, 30 other benign lesions, seven high-risk lesions, and 17 malignant lesions were identified as underlying causes of pathologic nipple discharge. Only eight of the 17 malignancies were detected on the initial US, while the remaining nine malignancies were detected by ductography. Among the nine malignancies detected by ductography, eight were detected on post-ductography US and could be localized for US-guided intervention. The sensitivities of ductography (94.1% [16/17]) and post-ductography US (94.1% [16/17]) were significantly higher than those of initial US (47.1% [8/17]; p = 0.027 and 0.013, respectively). The negative predictive value of post-ductography US (96.9% [31/32]) was significantly higher than that of the initial US (83.3% [45/54]; p = 0.006). Specificity was significantly higher for initial US than for ductography and post-ductography US (p = 0.001 for all). Conclusion: The combined use of ductography and US has a high sensitivity for detecting malignancy in patients with pathologic nipple discharge and negative mammography. Ductography findings enable lesion localization on second-look post-ductography US, thus facilitating the selection of optimal treatment plans.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.1-17
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• 2015

In this paper, we report what we have observed with regards to a prediction model for the military based on enlisted men's internal(cumulative records) and external data(SNS data). This work is significant in the military's efforts to supervise them. In spite of their effort, many commanders have failed to prevent accidents by their subordinates. One of the important duties of officers' work is to take care of their subordinates in prevention unexpected accidents. However, it is hard to prevent accidents so we must attempt to determine a proper method. Our motivation for presenting this paper is to mate it possible to predict accidents using enlisted men's internal and external data. The biggest issue facing the military is the occurrence of accidents by enlisted men related to maladjustment and the relaxation of military discipline. The core method of preventing accidents by soldiers is to identify problems and manage them quickly. Commanders predict accidents by interviewing their soldiers and observing their surroundings. It requires considerable time and effort and results in a significant difference depending on the capabilities of the commanders. In this paper, we seek to predict accidents with objective data which can easily be obtained. Recently, records of enlisted men as well as SNS communication between commanders and soldiers, make it possible to predict and prevent accidents. This paper concerns the application of data mining to identify their interests, predict accidents and make use of internal and external data (SNS). We propose both a topic analysis and decision tree method. The study is conducted in two steps. First, topic analysis is conducted through the SNS of enlisted men. Second, the decision tree method is used to analyze the internal data with the results of the first analysis. The dependent variable for these analysis is the presence of any accidents. In order to analyze their SNS, we require tools such as text mining and topic analysis. We used SAS Enterprise Miner 12.1, which provides a text miner module. Our approach for finding their interests is composed of three main phases; collecting, topic analysis, and converting topic analysis results into points for using independent variables. In the first phase, we collect enlisted men's SNS data by commender's ID. After gathering unstructured SNS data, the topic analysis phase extracts issues from them. For simplicity, 5 topics(vacation, friends, stress, training, and sports) are extracted from 20,000 articles. In the third phase, using these 5 topics, we quantify them as personal points. After quantifying their topic, we include these results in independent variables which are composed of 15 internal data sets. Then, we make two decision trees. The first tree is composed of their internal data only. The second tree is composed of their external data(SNS) as well as their internal data. After that, we compare the results of misclassification from SAS E-miner. The first model's misclassification is 12.1%. On the other hand, second model's misclassification is 7.8%. This method predicts accidents with an accuracy of approximately 92%. The gap of the two models is 4.3%. Finally, we test if the difference between them is meaningful or not, using the McNemar test. The result of test is considered relevant.(p-value : 0.0003) This study has two limitations. First, the results of the experiments cannot be generalized, mainly because the experiment is limited to a small number of enlisted men's data. Additionally, various independent variables used in the decision tree model are used as categorical variables instead of continuous variables. So it suffers a loss of information. In spite of extensive efforts to provide prediction models for the military, commanders' predictions are accurate only when they have sufficient data about their subordinates. Our proposed methodology can provide support to decision-making in the military. This study is expected to contribute to the prevention of accidents in the military based on scientific analysis of enlisted men and proper management of them.