• The KIPS Transactions:PartC
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• v.16C no.6
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• pp.699-706
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• 2009

The convergence tendency accelerates the realization of the ubiquitous healthcare (u-Healthcare) between the technology including the power generaation and IT-BT-NT of the ubiquitous computing technology. By rapidly analyzing a large amount of collected from the sensor network with processing and delivering to the medical team an u-Healthcare can provide a patient for an inappropriate regardless of the time and place. As to the existing u-Healthcare, since the sensor node all transmitted collected data by using with the Zigbee protocol the processing burden of the base node was big and there was many communication frequency of the sensor node. In this paper, the u-Healthcare system in which it can efficiently apply to mobile apparatuses it provided the transfer rate in which it is superior to the bio-signal delivery where there are the life and direct relation which by using the Bluetooth instead of the Zigbee protocol and in which it is variously used in the ubiquitous environment was designed. Moreover, by applying the EEF(Embedded Event Filtering) technique in which data in which it includes in the event defined in advance selected and it transmits with the base node, the communication frequency and were reduced. We confirmed to be the system in which it is efficient through the simulation result than the existing Electrocardiogram Measurement system.

• Journal of Digital Convergence
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• v.13 no.4
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• pp.227-233
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• 2015

The recent health care is growing rapidly want to receive offers users a variety of medical services, can be exploited easily exposed to a third party information on the role of the patient's hospital staff (doctors, nurses, pharmacists, etc.) depending on the patient clearly may have to be classified. In this paper, in order to ensure safe use by third parties in the health care environment, classify the attributes of patient information and patient privacy protection technique using hierarchical multi-property rights proposed to classify information according to the role of patient hospital officials The. Hospital patients and to prevent the proposed method is represented by a mathematical model, the information (the data consumer, time, sensor, an object, duty, and the delegation circumstances, and so on) the privacy attribute of a patient from being exploited illegally patient information from a third party the prevention of the leakage of the privacy information of the patient in synchronization with the attribute information between the parties.

• Korea Journal of Hospital Management
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• v.2 no.1
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• pp.162-182
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• 1997

This study was carried out to assess the current status of hospital wage structure and to find out the characteristics and problems in the current hospital wage structure. so as to provide empirical data for establishing a rational wage structure. The data were collected from administrative personnels in charge of wage management in 31 hospitals by using a structured questionnaire through direct visiting and mailing in Pusan Metropolitan City. The major findings in this study were as follows: First, the hospital wage structure applied differently to the basic wages between doctors and the other employees. The wage structure for doctors included performance rate of 51.6%, followed by a synthesis rate of 29.0%, while the wage for the other employees had the synthesis rate of 74.2%, followed by the seniority rate of 12.9%. Second, the wage consisted of a basic wage for 57.5%. the allowance for 21.1% and monthly installed bonus for 21.4%, and the basic wage comprised 68.3% of the total wage for doctors, as compared to 51.9% for nurses and medical technicians and 52.4% for administrative and managerial personnel. The annual rate of the bonus was average 460%, and 96.8% of the hospital did not consider personnel preformance appraisal when paying the bonus. Third, 80.6% of the hospitals applied the legal rate to the retirement allowance while 19.4% applying cumulative rates more than the legal rate, and all of university hospitals applied cumulative rates. Retirement reserves were practiced only in 54.9% of the hospitals. Forth, many hospitals seemed to be interested in applying graded wage system according to performance, by showing that 42.9% of the hospitals were planning to apply it in the future, despite only 9.7% practicing it. Fifth, the wage structure appeared to be complicated due to various kinds of allowances. The kind of the allowances varied among hospitals, ranging from 2 to 26 kinds, and increased as the size of hospital was larger. Sixth, the opinions leading to improve the basic wage structure favored the seniority rate for 51.6% either to maintain the present seniority rate(16.1%) or to apply the incentive pay in addition to the senior rate(35.5%). and also favored the performance rate for 35.5%, followed by the job rate for 12.9%. In conclusion, the current hospital wage structure seemed to be too complicated to reflect personal ability, contribution and performance and to become a big barrier to inducing worker's motivation and to strengthening in competitveness. Therefore it is suggested that the current wage structure should be revised to the one emphasizing on job and ability base with considering characteristics and situation of the hospital, rather than seniority factors.

• Journal of radiological science and technology
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• v.38 no.4
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• pp.375-381
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• 2015

Digital Radiography is a big part of diagnostic radiology. Because uncorrected digital radiography image supported false effect of Patient's health care. We must be manage the correct digital radiography image. Thus, the artifact images can have effect to make a wrong diagnosis. We report types of occurrence by analyzing the artifacts that occurs in digital radiography system. We had collected the artifacts occurred in digital radiography system of general hospital from 2007 to 2014. The collected data had analyzed and then had categorize as the occurred causes. The artifacts could be categorized by hardware artifacts, software artifacts, operating errors, system artifacts, and others. Hardware artifact from a Ghost artifact that is caused by lag effect occurred most frequently. The others cases are the artifacts caused by RF noise and foreign body in equipments. Software artifacts are many different types of reasons. The uncorrected processing artifacts and the image processing error artifacts occurred most frequently. Exposure data recognize (EDR) error artifacts, the processing error of commissural line, and etc., the software artifacts were caused by various reasons. Operating artifacts were caused when the user didn't have the full understanding of the digital medical image system. System artifacts had appeared the error due to DICOM header information and the compression algorithm. The obvious artifacts should be re-examined, and it could result in increasing the exposure dose of the patient. The unclear artifact leads to a wrong diagnosis and added examination. The ability to correctly determine artifact are required. We have to reduce the artifact occurrences by understanding its characteristic and providing sustainable education as well as the maintenance of the equipments.

• Research in Community and Public Health Nursing
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• v.11 no.2
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• pp.399-410
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• 2000

In this, analyzing the type of subjectivity in which people would have about home visit nursing services originating from public health care centers. I tried to research more effective ways to improve home visit nursing care services. and later. for the development of home visit nursing care. to supply basic data. The method for this study was the Q-method. created by William Stephenson. and was adequate for the study of subjectivity. For this study. through the deep interview. literature inquiry, and the discussion course. 206 Q-statement sentences were abstracted. and based on them, after Q-sample-selection. I then collected the Q-categorized-result from 32 subjects from Mar. 10. 2000 to Mar. 25. 2000. Through the statistic a analysis of PC-Qunal program. the subjectivity species were categorized and analyzed. The study results show that there are 3 sorts of recognition types. and they are analyzed in the following; The first type: the positively receiving type shows that they feel thankful and a trusting feeling about home visit nursing. The second type: the negatively mistrusting type shows that they had doubtful attitudes about the specialty of home visit nursing: they wanted medicine or nutrition remedies rather than health education and concerning the their own health care, they prefered the hospitals or clinics. The third type: the conditional receiving type shows that even though they had a positive receiving attitude about home visit nursing wanting to consult with the home visit nurses about the difficult problem which could not easily be settled, hoping that the home visit nurses could visit them more often, in their actual lives. they strongly indicated their attitudes concerning money as more important than home visits. The subjects in these 3 types commonly had a good feeling about the kindness of the home visit nurses: the first and third types also had a positive recognition about home visit nursing; however. in aspects of the evaluation and receiving attitudes, they showed a big difference. When all the above results are integrated. in the case of the first type the home visit nursing service, which satisfied the demand for health care of the medically weak people. should be continuously supplied. Additionally in case of the second type (negatively mistrust). continuous education and support should be supplied with enough interest to lead their concerns about their own health care as well as lead medical spending in a productive and effective direction in order to change their impressions. Through this study. I learned that the recognition of the objectives of home visit nursing services can be categorized in to 3 types and could be analyzed. Thus I wish that this study helps to present basic data which contributes to the development of the home visit nursing field.

• Journal of vocational education research
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• v.37 no.1
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• pp.101-118
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• 2018

The purpose of this study was to investigate the effect size of the difference of job satisfaction by type of employment by combining data from previous studies. For this purpose, the total of 95 articles analyzed. For the analysis of data, CMA(Comprehensive Meta-Analysis) 2.0 program was used and statistical significance was set at 5%(${\alpha}=0.05$). The main conclusions of this study are as follows. First, regular workers have higher job satisfaction than non-regular workers and the effect size of employment type is medium. Second, among five constituents of job satisfaction, the difference of wage and promotion satisfaction is greater than the difference of satisfaction in human relations, work and working environment satisfaction. Third, the job satisfaction of regular and non-regular workers differs according to the occupation areas. Fourth, there is a big difference in job satisfaction in financial, insurance, food and service occupation areas, and regular workers have higher job satisfaction than non - regular workers. On the other hand, non-regular workers have higher job satisfaction than regular workers in health, medical, social occupation areas.

• Journal of Digital Convergence
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• v.18 no.12
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• pp.425-434
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• 2020

Background: Viral infection outbreaks are emerging public health concerns. They often exhibit seasonal patterns that could be predicted by the application of big data and bioinformatic analyses. Purpose: The purpose of this study was to identify trends in diarrhea-causing viruses such as rotavirus (Gr.A), norovirus G-I, and norovirus G-II in Cheonan, Korea. The identified related factors of diarrhea-causing viruses may be used to predict their trend and prevent their infections. Method: A retrospective analysis of 4,009 fecal samples from June 2010 to December 2019 was carried out at Dankook University Hospital in Cheonan. Reverse transcription-PCR (RT-PCR) was employed to identify virus strains. Information about seasonal patterns of infection was extracted and compared with local weather data. Results: Out of the 4,009 fecal samples tested using multiplex RT-PCR (mRT-PCR), 985 were positive for infection with Gr.A, G-I, and G-II. Out of these 985 cases, 95.3% (n = 939) were under 10 years of age. Gr.A, G-I, and G-II showed high infection rates in patients under 10 years of age. Student's t-test showed a significant correlation between the detection rate of Gr.A and the relative humidity. The detection rate of G-II significantly correlated with wind-chill temperature. Conclusion: Climate factors differentially modulate rotavirus and norovirus infection patterns. These observations provide novel insights into the seasonal impact on the pathogenesis of Gr.A, G-I, and G-II.

• Tuberculosis and Respiratory Diseases
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• v.85 no.1
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• pp.74-79
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• 2022

Background: The effect of underlying chronic obstructive pulmonary disease (COPD) on coronavirus disease 2019 (COVID-19) during a pandemic is controversial. The purpose of this study was to examine the prognosis of COVID-19 according to the underlying COPD. Methods: COVID-19 patients were assessed using nationwide health insurance data. Comorbidities were evaluated using the modified Charlson Comorbidity Index (mCCI) which excluded COPD from conventional CCI scores. Baseline characteristics were assessed. Univariable and multiple logistic and linear regression analyses were performed to determine effects of variables on clinical outcomes. Ages, sex, mCCI, socioeconomic status, and underlying COPD were selected as variables. Results: COPD patients showed older age (71.3±11.6 years vs. 47.7±19.1 years, p<0.001), higher mCCI (2.6±1.9 vs. 0.8±1.3, p<0.001), and higher mortality (22.9% vs. 3.2%, p<0.001) than non-COPD patients. The intensive care unit admission rate and hospital length of stay were not significantly different between the two groups. All variables were associated with mortality in univariate analysis. However, underlying COPD was not associated with mortality unlike other variables in the adjusted analysis. Older age (odds ratio [OR], 1.12; 95% confidence interval [CI], 1.11-1.14; p<0.001), male sex (OR, 2.29; 95% CI, 1.67-3.12; p<0.001), higher mCCI (OR, 1.30; 95% CI, 1.20-1.41; p<0.001), and medical aid insurance (OR, 1.55; 95% CI, 1.03-2.32; p=0.035) were associated with mortality. Conclusion: Underlying COPD is not associated with a poor prognosis of COVID-19.

• Radiation Oncology Journal
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• v.17 no.2
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• pp.172-178
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• 1999

Purpose : To measure the basic structural characteristics of radiation oncology facilities in Korea during 1997 and to compare personnel, equipments and patient loads between Korea and developed countries. Methods and Materials : Mail serveys we conducted in 1998 and data on treatment machines, personnel and peformed new patients were collected. Responses were obtained from the 100 percent of facilities. The consensus data of the whole country were summarized using Microsoft Excel program. Results: In Korea during 1997, 42 facilities delivered megavoltage radiation theraphy with 71 treatment machines, 100 radiation oncologists, 26 medical physicist, 205 technologists and 19,773 new patients. Eighty nine percent of facilities in Korea had linear accelators at least 6 MeV maximum photon energy. Ninety five percent of facilities had simulators while five percent of facilities had no simulator, Ninety one percent of facilities had computer planning systems and eighty three percent of facilities reported that they had a written quality assurance program. Thirty six percent of facilities had only one radiation oncologist and thirty eight percent of facilities had no medical physicists. The median of the distribution of annual patients load of a facility, patients load per a machine, patients load per a radiation oncologist, patients load per a therapist and therapists per a machine in Korea were 348 patients per a year, 263 patients per a machine, 171 patients per a radiation oncologist, 81 patients per a therapist, and 3 therapists per a machine respectively. Conclusions : The whole scale of the radiation oncology departments in Korea was smaller than Japan and USA in population ratio regard. In case of hardware level like linear accelerators, simulators and computer planning systems, there was no big differences between Korea and USA. The patients loads of radiation oncologists and therapists had no significant differences as compared with USA. However, it was desirable to consider the part time system in USA because there were a lot of hospitals which did not employ medical physicists.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.23-45
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• 2020

Big data is creating in a wide variety of fields such as medical care, manufacturing, logistics, sales site, SNS, and the dataset characteristics are also diverse. In order to secure the competitiveness of companies, it is necessary to improve decision-making capacity using a classification algorithm. However, most of them do not have sufficient knowledge on what kind of classification algorithm is appropriate for a specific problem area. In other words, determining which classification algorithm is appropriate depending on the characteristics of the dataset was has been a task that required expertise and effort. This is because the relationship between the characteristics of datasets (called meta-features) and the performance of classification algorithms has not been fully understood. Moreover, there has been little research on meta-features reflecting the characteristics of multi-class. Therefore, the purpose of this study is to empirically analyze whether meta-features of multi-class datasets have a significant effect on the performance of classification algorithms. In this study, meta-features of multi-class datasets were identified into two factors, (the data structure and the data complexity,) and seven representative meta-features were selected. Among those, we included the Herfindahl-Hirschman Index (HHI), originally a market concentration measurement index, in the meta-features to replace IR(Imbalanced Ratio). Also, we developed a new index called Reverse ReLU Silhouette Score into the meta-feature set. Among the UCI Machine Learning Repository data, six representative datasets (Balance Scale, PageBlocks, Car Evaluation, User Knowledge-Modeling, Wine Quality(red), Contraceptive Method Choice) were selected. The class of each dataset was classified by using the classification algorithms (KNN, Logistic Regression, Nave Bayes, Random Forest, and SVM) selected in the study. For each dataset, we applied 10-fold cross validation method. 10% to 100% oversampling method is applied for each fold and meta-features of the dataset is measured. The meta-features selected are HHI, Number of Classes, Number of Features, Entropy, Reverse ReLU Silhouette Score, Nonlinearity of Linear Classifier, Hub Score. F1-score was selected as the dependent variable. As a result, the results of this study showed that the six meta-features including Reverse ReLU Silhouette Score and HHI proposed in this study have a significant effect on the classification performance. (1) The meta-features HHI proposed in this study was significant in the classification performance. (2) The number of variables has a significant effect on the classification performance, unlike the number of classes, but it has a positive effect. (3) The number of classes has a negative effect on the performance of classification. (4) Entropy has a significant effect on the performance of classification. (5) The Reverse ReLU Silhouette Score also significantly affects the classification performance at a significant level of 0.01. (6) The nonlinearity of linear classifiers has a significant negative effect on classification performance. In addition, the results of the analysis by the classification algorithms were also consistent. In the regression analysis by classification algorithm, Naïve Bayes algorithm does not have a significant effect on the number of variables unlike other classification algorithms. This study has two theoretical contributions: (1) two new meta-features (HHI, Reverse ReLU Silhouette score) was proved to be significant. (2) The effects of data characteristics on the performance of classification were investigated using meta-features. The practical contribution points (1) can be utilized in the development of classification algorithm recommendation system according to the characteristics of datasets. (2) Many data scientists are often testing by adjusting the parameters of the algorithm to find the optimal algorithm for the situation because the characteristics of the data are different. In this process, excessive waste of resources occurs due to hardware, cost, time, and manpower. This study is expected to be useful for machine learning, data mining researchers, practitioners, and machine learning-based system developers. The composition of this study consists of introduction, related research, research model, experiment, conclusion and discussion.