• Journal of Korean Academy of Fundamentals of Nursing
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• v.19 no.4
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• pp.444-452
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• 2012

Purpose: To explore the usefulness of previous fall history as a triage variable for inpatients. Methods: Medical records of 21,382 patients, admitted to medical units of one tertiary hospital, were analyzed retrospectively. Inpatient falls were identified from the hospital's self-report system. Non-falls in 1,125 patients were selected by a stratified matching sampling with 125 patients with falls (0.59%). A comparative and predictive accuracy analysis was conducted to describe differences between the two groups with and without a history of falls. Logistic regression was used to measure the effect size of the fall history. Results: The fall history group showed higher prevalence by 9 fold than the non-fall history group. The relationships between falls and relevant variables which were significant in the non-fall history group, were not significant for the fall history group. Falls in the fall history group were 25 times more likely than in the non-fall group. Predictive accuracy of the risk assessment tool showed almost zero specificity in the fall history group. Conclusion: The presence of fall history, the fall prevalence, variables relevant to falls, and the accuracy of the risk tool were different, which support the usefulness of the fall history as a triage variable.

• Nuclear Engineering and Technology
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• v.52 no.6
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• pp.1188-1200
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• 2020

In this paper, we consider a SDMS (Self-Diagnostic Monitoring System) for a reciprocating pump for the purpose of not only diagnosis but also prognosis. We have replaced a multi class estimator that selects only the most probable one with a multi label estimator such that we are able to see the state of each of the components. We have introduced a measure called certainty so that we are able to represent the symptom and its state. We have built a flow loop for a reciprocating pump system and presented some results. With these changes, we are not only able to detect both the dominant symptom as well as others but also to monitor how the degree of severity of each component changes. About the dominant ones, we found that the overall recognition rate of our algorithm is about 99.7% which is slightly better than that of the former SDMS. Also, we are able to see the trend and to make a base to find prognostics to estimate the remaining useful life. With this we hope that we have gone one step closer to the final goal of prognosis of SDMS.

• Research in Community and Public Health Nursing
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• v.29 no.1
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• pp.41-53
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• 2018

Purpose: This study was conducted to identify the factors affecting on unmet healthcare needs of married immigrant women, especially who are working in South Korea. Methods: It is designed as a cross-sectional descriptive study. We analyzed data from 8,142 working married immigrant women to the 'National Survey of Multicultural Families 2015.' Based on Andersen's health behavior model, logistic regression was conducted to determine the predictors of unmet healthcare need. Results: The prevalence of unmet healthcare needs among the subjects was 11.6%. In multivariate analysis, significant predictors of unmet needs included existence of preschooler, country of origin, period of residence in predisposing factors, monthly household income, helpful social relationship, social discrimination, Korean proficiency, working hour per week in enabling factors, and self-rated health, experience of grief or desperation in need factors. Conclusion: The association between labor-related factors and unmet healthcare needs of marriage immigrant women currently working was found from nationally representative sample. Support policies for immigrant women working more than legally defined hours and having preschooler should be supplemented to reduce unmet healthcare needs. In addition, eradicating discrimination in workplace, enlarging social relationship, and developing culturally competent nursing services tailored to health problems caused by labor are needed.

• Korea Journal of Hospital Management
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• v.23 no.3
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• pp.52-62
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• 2018

This study aims to analyze the factors affecting in-hospital complication and length of stay in elderly patients with total knee arthroplasty. A total of 8,224 inpatients over 65 years old were selected from the national old inpatient sample data which was produced by Health Insurance Review and Assessment Service in 2016. STATA 12.0 was performed using frequency, chi-square test, t-test, ANOVA and multiple linear and logistic regression analysis. Analysis results show that ages(over 85), Charlson Comorbidity Index, district(metropolitan) for general hospitals and gender, district, beds(100-199) for hospitals are significantly influenced in-hospital complication. Statistically significant factors affecting the length of stay are gender, insurance type, depression, district, bed(300 over) for general hospitals and gender, type of insurance, Charlson Comorbidity Index, depression, district, beds(200-299) for hospitals. Based on these findings, the factors affecting in-hospital complication and length of stay were different depending on the type of medical institution. Accordingly, policymakers should analyze the differences in care behavior depending on the type of medical institution and expand policy and financial support to resolve them.

• Journal of the Korea Convergence Society
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• v.11 no.7
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• pp.223-230
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• 2020

The purpose of this study was to identify the characteristics of college graduates who obtain qualifications, and to check the economic and ineffectiveness of qualifications through the analysis of employment and job relevance according to their school area. For the main research, the "2016 Graduates Occupational Mobility Survey (GOMS)" data was used to perform basic statistics and logistic regression analysis using SPSS 25. This study reaffirmed the effectiveness of employment as stated in the preceding study, and in particular, the qualification of non-Seoul area colleges students had some positive effects on employment and job compatibility. In conclusion, the effectiveness of qualifications according to individual attributes and location of school may differ, but it suggests that proper qualification encouragement and acquisition support can help the employment and job convergence of college graduates.

• ETRI Journal
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• v.42 no.3
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• pp.311-323
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• 2020

Encrypted traffic classification plays a vital role in cybersecurity as network traffic encryption becomes prevalent. First, we briefly introduce three traffic encryption mechanisms: IPsec, SSL/TLS, and SRTP. After evaluating the performances of support vector machine, random forest, naïve Bayes, and logistic regression for traffic classification, we propose the combined approach of entropy estimation and artificial neural networks. First, network traffic is classified as encrypted or plaintext with entropy estimation. Encrypted traffic is then further classified using neural networks. We propose using traffic packet's sizes, packet's inter-arrival time, and direction as the neural network's input. Our combined approach was evaluated with the dataset obtained from the Canadian Institute for Cybersecurity. Results show an improved precision (from 1 to 7 percentage points), and some application classification metrics improved nearly by 30 percentage points.

• Women's Health Nursing
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• v.6 no.2
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• pp.316-329
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• 2000

The purpose of this study is to identify various predictors of smoking behavior among female adolescents. Data for this study was collected from 357 students enrolled in a female high school, a coeducational high school, and a vocational high school in Seoul from 1st to 20th July 1999 and this data was analysed based on descriptive statistics and logistic regression with the SAS program. The results were as follows: 1. The proportion of current smokers was 17.9% and experienced smokers was 34.2% in girl high schools. 2. According to the factors family, school, peer, social learning, and psychological factors, the predictable variables are lack of family attachment and function in family factors, school involvement in school factors, associating with smokers among peers and modeling and differential reinforcement of smoking in social learning factors, self-assertiveness, self-esteem, and depression in psychological factors. 3. According to all the factors, the main predictors were peer association, differential reinforcement of smoking, and lack of family attachment. These variables cause 48.4% of smoking behavior. To prohibit smoking among female students, this society should develop a program to focus on peer leadership about quitting smoking, acquirement of skills of refusal for smoking, counter conditioning, reinforcement management, and support system about nonsmoking.

• Korean Journal of Adult Nursing
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• v.29 no.1
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• pp.32-40
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• 2017

Purpose: The purpose of this secondary data analysis was to identify factors influencing a relapse among patients with coronary artery disease (CAD). Methods: Of 250 participants enrolled in the original study 75 were selected as there was no relapse for more than one year following the initial treatment and 54 were selected because there was a relapse. Data were analyzed using ${\chi}^2$ test, t-test or F test to determine if there were any significant differences in the study variables relative to the status of relapse. Predictors were calculated by logistic regression. Results: Autonomy supported by healthcare providers was the significant predictor for relapse in patients with CAD. Patients with low autonomy supported by healthcare providers was 3.91 times more likely to relapse than patients with high autonomy supported. Patients with diabetes were at greater risk of recurrence. Conclusion: Secondary prevention of CAD is a major task for patients with CAD. Behavioral strategies for cardiovascular risk reduction are essential and autonomy supported by healthcare providers should be included in their strategies.

• Journal of Soil and Groundwater Environment
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• v.21 no.3
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• pp.21-34
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• 2016

In the present study, we applied various machine learning techniques comparatively for prediction of subsurface structures based on multiple secondary information (i.e., well-logging data). The machine learning techniques employed in this study are Naive Bayes classification (NB), artificial neural network (ANN), support vector machine (SVM) and logistic regression classification (LR). As an alternative model, conventional hidden Markov model (HMM) and modified hidden Markov model (mHMM) are used where additional information of transition probability between primary properties is incorporated in the predictions. In the comparisons, 16 boreholes consisted with four different materials are synthesized, which show directional non-stationarity in upward and downward directions. Futhermore, two types of the secondary information that is statistically related to each material are generated. From the comparative analysis with various case studies, the accuracies of the techniques become degenerated with inclusion of additive errors and small amount of the training data. For HMM predictions, the conventional HMM shows the similar accuracies with the models that does not relies on transition probability. However, the mHMM consistently shows the highest prediction accuracy among the test cases, which can be attributed to the consideration of geological nature in the training of the model.

• Journal of the Korean Society of School Health
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• v.29 no.3
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• pp.267-276
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• 2016

Purpose: This study was conducted to identify the factors of suicidal ideation in middle school students by gender, and to announce the necessity of gaining upon a socio-cultural comprehension-based approach. Methods: Three middle schools were randomly selected from a pool of 21 middle schools in S City, and were stratified by school and grade. A total of 878 students were included in the analysis. Multiple logistic regression analysis was conducted to identify the factors that affect suicidal ideation by gender. Results: Suicidal ideation and depression scores of the female students were over twofold higher than those of the male students. The factor that was found to affect suicidal ideation in the male students was depression while for the female students the factors were visiting suicide-related websites, having friends who attempted to commit suicide, depression, and poor communication with one's parents. Conclusion: The findings in this study support gender differences in suicidal ideation, and suggest that building socio-cultural environments are needed to abate their negative emotions and to help youth find out their reasons to live.