• Quality Improvement in Health Care
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• v.28 no.1
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• pp.45-54
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• 2022

Purpose: The study aims to identify the theme list and priority criteria of patient safety theme reports in South Korea. Methods: The survey was conducted twice, and the importance of each criterion and theme was measured on a nine-point scale using the Delphi technique by a panel of 19 patient safety experts. The criteria included severity, universality, preventability, and organizational-social impact. Descriptive statistics such as frequency, percentage, mean, standard deviation, median, and interval quartile range were used to analyze the data. Results: The parameters were assigned a weighted average of 35% for severity, 20% for universality, 30% for preventability, and 15% for organizational-social impact, respectively. The final top three rankings were surgery safety, blood transfusion safety, and medication safety. In addition to expert opinion, for the theme that is selected based on the priority ranking, one to five sub-topics can be derived from the theme based on the priority ranking, societal demands, or the yearly priority list of patient safety incidents. Conclusion: It is recommended that the official patient safety center distribute the report in the form of a summary that can be utilized nationwide at medical institutions, government institutions, and other places. Updates, as well as accumulated theme reports, will serve as the baseline data for the proposal of the system and for the policy designed to implement and improve institutions' safety practices as a standard of domestic patient safety practice guidelines.

• Journal of the Korean Geotechnical Society
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• v.38 no.3
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• pp.35-42
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• 2022

The N-value from the Standard Penetration Test (SPT), which is one of the representative in-situ test, is an important index that provides basic geological information and the depth of the bearing layer for the design of geotechnical structures. In the aspect of time and cost-effectiveness, there is a need to carry out a representative sampling test. However, the various variability and uncertainty are existing in the soil layer, so it is difficult to grasp the characteristics of the entire field from the limited test results. Thus the spatial interpolation techniques such as Kriging and IDW (inverse distance weighted) have been used for predicting unknown point from existing data. Recently, in order to increase the accuracy of interpolation results, studies that combine the geotechnics and deep learning method have been conducted. In this study, based on the SPT results of about 22,000 holes of ground survey, a comparative study was conducted to predict the depth of the bearing layer using deep learning methods and IDW. The average error among the prediction results of the bearing layer of each analysis model was 3.01 m for IDW, 3.22 m and 2.46 m for fully connected network and PointNet, respectively. The standard deviation was 3.99 for IDW, 3.95 and 3.54 for fully connected network and PointNet. As a result, the point net deep learing algorithm showed improved results compared to IDW and other deep learning method.

• Journal of radiological science and technology
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• v.34 no.3
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• pp.239-244
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• 2011

In this study, we evaluated diffusion weighted imaging (DWI) to investigate whether the DWI parameters can predict characteristic parameters on pathologic specimens of tumor or not. CFPAC-1 was injected subcutaneously on the back flank of athymic nude mice (n=13) then two tumors were initiated on each mouse (2${\times}$13=26 tumors). The mice were sacrificed to make specimen immediately after initial MR imaging then were compared with the MR image. A dedicated high-field (7T) small-animal MR scanner was used for image acquisitions. A T1 and T2 weighted axial image using RARE technique was acquired to measure the T2 values and tumor size. DWI MR was performed for calculating ADC values. To evaluate tumor cellularity and determine the levels of MVD, tumor cells were excised and processed for H-E staining and immunostaining using CD31. T2 values and ADC values were computed and analyzed for each half of the tumors and compared to the correlated specimens slide. Median ADC within each half of mass was compared to the cellularity and MVD in the correlated area of pathologic slide. The mean of ADC value is $0.7327{\times}10^{-3}$ $mm^2/s$ and standard deviation is $0.1075{\times}10^{-3}$ $mm^2/s$. There is a linear relationship between ADC value and tumor necrosis (R2=0.697, p< 0.001). DW image parameters including the ADC values can be utilized as surrogate markers to assess intratumoral neoangiogenesis and change of the internal structure of tumor cells.

• Journal of Preventive Medicine and Public Health
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• v.11 no.1
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• pp.123-127
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• 1978

The major objective of this research is to identify those hospital characteristics that best explain cost variation among hospitals and to formulate linear models that can predict hospital costs. Specific emphasis is placed on hospital output, that is, the identification of diagnosis related patient groups (DRGs) which are medically meaningful and demonstrate similar patterns of hospital resource consumption. A casemix index is developed based on the DRGs identified. Considering the common problems encountered in previous hospital cost research, the following study requirements are estab-lished for fulfilling the objectives of this research: 1. Selection of hospitals that exercise similar medical and fiscal practices. 2. Identification of an appropriate data collection mechanism in which demographic and medical characteristics of individual patients as well as accurate and comparable cost information can be derived. 3. Development of a patient classification system in which all the patients treated in hospitals are able to be split into mutually exclusive categories with consistent and stable patterns of resource consumption. 4. Development of a cost finding mechanism through which patient groups' costs can be made comparable across hospitals. A data set of Medicare patients prepared by the Social Security Administration was selected for the study analysis. The data set contained 27,229 record abstracts of Medicare patients discharged from all but one short-term general hospital in Connecticut during the period from January 1, 1971, to December 31, 1972. Each record abstract contained demographic and diagnostic information, as well as charges for specific medical services received. The 'AUT-OGRP System' was used to generate 198 DRGs in which the entire range of Medicare patients were split into mutually exclusive categories, each of which shows a consistent and stable pattern of resource consumption. The 'Departmental Method' was used to generate cost information for the groups of Medicare patients that would be comparable across hospitals. To fulfill the study objectives, an extensive analysis was conducted in the following areas: 1. Analysis of DRGs: in which the level of resource use of each DRG was determined, the length of stay or death rate of each DRG in relation to resource use was characterized, and underlying patterns of the relationships among DRG costs were explained. 2. Exploration of resource use profiles of hospitals; in which the magnitude of differences in the resource uses or death rates incurred in the treatment of Medicare patients among the study hospitals was explored. 3. Casemix analysis; in which four types of casemix-related indices were generated, and the significance of these indices in the explanation of hospital costs was examined. 4. Formulation of linear models to predict hospital costs of Medicare patients; in which nine independent variables (i. e., casemix index, hospital size, complexity of service, teaching activity, location, casemix-adjusted death. rate index, occupancy rate, and casemix-adjusted length of stay index) were used for determining factors in hospital costs. Results from the study analysis indicated that: 1. The system of 198 DRGs for Medicare patient classification was demonstrated not only as a strong tool for determining the pattern of hospital resource utilization of Medicare patients, but also for categorizing patients by their severity of illness. 2. The wei틴fed mean total case cost (TOTC) of the study hospitals for Medicare patients during the study years was $11,27.02 with a standard deviation of $117.20. The hospital with the highest average TOTC ($1538.15) was 2.08 times more expensive than the hospital with the lowest average TOTC ($743.45). The weighted mean per diem total cost (DTOC) of the study hospitals for Medicare patients during the sutdy years was $107.98 with a standard deviation of $15.18. The hospital with the highest average DTOC ($147.23) was 1.87 times more expensive than the hospital with the lowest average DTOC ($78.49). 3. The linear models for each of the six types of hospital costs were formulated using the casemix index and the eight other hospital variables as the determinants. These models explained variance to the extent of 68.7 percent of total case cost (TOTC), 63.5 percent of room and board cost (RMC), 66.2 percent of total ancillary service cost (TANC), 66.3 percent of per diem total cost (DTOC), 56.9 percent of per diem room and board cost (DRMC), and 65.5 percent of per diem ancillary service cost (DTANC). The casemix index alone explained approximately one half of interhospital cost variation: 59.1 percent for TOTC and 44.3 percent for DTOC. Thsee results demonstrate that the casemix index is the most importand determinant of interhospital cost variation Future research and policy implications in regard to the results of this study is envisioned in the following three areas: 1. Utilization of casemix related indices in the Medicare data systems. 2. Refinement of data for hospital cost evaluation. 3. Development of a system for reimbursement and cost control in hospitals.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.65-82
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• 2021

Stock market investors are generally split into foreign investors, institutional investors, and individual investors. Compared to individual investor groups, professional investor groups such as foreign investors have an advantage in information and financial power and, as a result, foreign investors are known to show good investment performance among market participants. The purpose of this study is to propose an investment strategy that combines investor-specific transaction information and machine learning, and to analyze the portfolio investment performance of the proposed model using actual stock price and investor-specific transaction data. The Korea Exchange offers daily information on the volume of purchase and sale of each investor to securities firms. We developed a data collection program in C# programming language using an API provided by Daishin Securities Cybosplus, and collected 151 out of 200 KOSPI stocks with daily opening price, closing price and investor-specific net purchase data from January 2, 2007 to July 31, 2017. The self-organizing map model is an artificial neural network that performs clustering by unsupervised learning and has been introduced by Teuvo Kohonen since 1984. We implement competition among intra-surface artificial neurons, and all connections are non-recursive artificial neural networks that go from bottom to top. It can also be expanded to multiple layers, although many fault layers are commonly used. Linear functions are used by active functions of artificial nerve cells, and learning rules use Instar rules as well as general competitive learning. The core of the backpropagation model is the model that performs classification by supervised learning as an artificial neural network. We grouped and transformed investor-specific transaction volume data to learn backpropagation models through the self-organizing map model of artificial neural networks. As a result of the estimation of verification data through training, the portfolios were rebalanced monthly. For performance analysis, a passive portfolio was designated and the KOSPI 200 and KOSPI index returns for proxies on market returns were also obtained. Performance analysis was conducted using the equally-weighted portfolio return, compound interest rate, annual return, Maximum Draw Down, standard deviation, and Sharpe Ratio. Buy and hold returns of the top 10 market capitalization stocks are designated as a benchmark. Buy and hold strategy is the best strategy under the efficient market hypothesis. The prediction rate of learning data using backpropagation model was significantly high at 96.61%, while the prediction rate of verification data was also relatively high in the results of the 57.1% verification data. The performance evaluation of self-organizing map grouping can be determined as a result of a backpropagation model. This is because if the grouping results of the self-organizing map model had been poor, the learning results of the backpropagation model would have been poor. In this way, the performance assessment of machine learning is judged to be better learned than previous studies. Our portfolio doubled the return on the benchmark and performed better than the market returns on the KOSPI and KOSPI 200 indexes. In contrast to the benchmark, the MDD and standard deviation for portfolio risk indicators also showed better results. The Sharpe Ratio performed higher than benchmarks and stock market indexes. Through this, we presented the direction of portfolio composition program using machine learning and investor-specific transaction information and showed that it can be used to develop programs for real stock investment. The return is the result of monthly portfolio composition and asset rebalancing to the same proportion. Better outcomes are predicted when forming a monthly portfolio if the system is enforced by rebalancing the suggested stocks continuously without selling and re-buying it. Therefore, real transactions appear to be relevant.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.8
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• pp.195-204
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• 2020

The purpose of this study was to explore factors associated with smartphone dependence among adults in their 20s. The data were derived from the 2017 Survey on Smartphone Over-dependence conducted by the Ministry of Science and ICT and the National Information Society Agency. There were 3,684 participants. The data were analyzed by frequency, percentage, mean, standard deviation, independent t-test, Pearson's correlation coefficient, and weighted hierarchical multiple regression analysis. For the results, factors related with higher smartphone dependence of participants were duration (β=.18, p=.000) and frequency (β=.04, p=.000) of usage, gaming (β=.15, p=.000), watching videos (β=.09, p=.000), mobile shopping (β=.05, p=.000), working (β=.05, p=.010), e-mailing (β=.13, p=.000), and sports betting (β=.07, p=.000). Music (β=-.07, p=.000) and adult content (β=-.07, p=.000) significantly reduced their smartphone dependence. SNS (Social Networking Services) (β=.01, p=.358) and instant messengers (β=-.02, p=.330) were not factors related to smartphone dependence. However, instant messengers were the most used by participants and had a strong correlation with working (r=.55, p=.000). This study shows that smartphone usage patterns related with smartphone dependence among adults in their 20s are different from those of children and adolescents. These results could be used to more deeply understand smartphone dependence among adults in their 20s and plan early detection and prevention and care of dependence.

• Asian-Australasian Journal of Animal Sciences
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• v.25 no.8
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• pp.1073-1082
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• 2012

We first sought to clarify the effects of discounted rate, survival rate, and lactation persistency as a component trait of the selection index on net merit, defined as the first five lactation milks and herd life (HL) weighted by 1 and 0.389 (currently used in Japan), respectively, in units of genetic standard deviation. Survival rate increased the relative economic importance of later lactation traits and the first five lactation milk yields during the first 120 months from the start of the breeding scheme. In contrast, reliabilities of the estimated breeding value (EBV) in later lactation traits are lower than those of earlier lactation traits. We then sought to clarify the effects of applying single nucleotide polymorphism (SNP) on net merit to improve the reliability of EBV of later lactation traits to maximize their increased economic importance due to increase in survival rate. Net merit, selection accuracy, and HL increased by adding lactation persistency to the selection index whose component traits were only milk yields. Lactation persistency of the second and (especially) third parities contributed to increasing HL while maintaining the first five lactation milk yields compared with the selection index whose only component traits were milk yields. A selection index comprising the first three lactation milk yields and persistency accounted for 99.4% of net merit derived from a selection index whose components were identical to those for net merit. We consider that the selection index comprising the first three lactation milk yields and persistency is a practical method for increasing lifetime milk yield in the absence of data regarding HL. Applying SNP to the second- and third-lactation traits and HL increased net merit and HL by maximizing the increased economic importance of later lactation traits, reducing the effect of first-lactation milk yield on HL (genetic correlation ($r_G$) = -0.006), and by augmenting the effects of the second- and third-lactation milk yields on HL ($r_G$ = 0.118 and 0.257, respectively).

• Journal of Korea Water Resources Association
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• v.36 no.4
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• pp.533-545
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• 2003

This study analyzed several storm events observed in the Seolma-chun basin to derive the characteristic velocity of GIUH (Geomophological Instantaneous Unit Hydrograph) as well as its variability. Especially, this study focused on the variation of characteristic velocity due to the change of rainfall characteristics. The IUH of the Seolma-chun basin was derived using the HEC-1, whose peak discharge and time were then compared with those of the GIUH to derive the characteristic velocities. The characteristics velocities were analyzed by comparing with the GcIUH (Geomorphoclimatic IUH) as well as the characteristics of rainfall. Results are summarized as follows. (1) The characteristic velocity of GIUH was estimated higher with higher variability than the GcIUH, but their trends were found similar (2) Total amount of effective rainfall (or, mean effective rainfall) well explains the characteristic velocity of GIUH. This could be assured by the regression analysis, whose coefficient of determination was estimated about 0.6. (3) The duration and the maximum intensity of rainfall were found not to affect significantly on the characteristic velocity of GIUH. The coefficients of determination were estimated less than 0.3 for all cases considered. (4) For the rainfall events used in this study, the characteristic velocities of GIUH were found to follow the Gaussian distribution with its mean and the standard deviation 0.402 m/s and 0.173 m/s, respectively. Most of the values are within the range of 0.4∼0.5 m/s, and its coefficient of variation was estimated to be 0.43, much less than that of the runoff itself (about 1.0).

• Korean Journal of Breeding Science
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• v.41 no.3
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• pp.228-235
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• 2009

Growth and branch characteristics of 35 half-sib families were surveyed in a seedling seed orchard of Quercus acutissima at ages 10 and 12. The averages of height, DBH (diameter at breast height), branch height, crown width, branch angle and stem straightness at age 12 were 9.96 m, 14.50 cm, 1.04 m, 6.80 m, $18.82^{\circ}$ and 2.58, respectively. Families of 075 and 052 showed superior height growth and 0511 and 0517 were inferior ones. For DBH growth, 075 and 0413 were best families and 0725 and 0511 were inferior families. Pearson's product moment and Spearman's rank correlation coefficients were all positive for all growth traits except branch angle at ages 10 and 12. This result showed that the families with good height and DBH growth were also superior in stem straightness. In ANOVA, there was a highly significant difference among families in height, DBH, cylindric volume and stem straightness. Branch height, crown width and branch angle were also significantly different among families. Family heritability was higher than individual heritability at ages 10 and 12. Height, DBH and stem straightness were under strong genetic control, showing high family heritability. This implies that high genetic gain could be expected by family selection. Expected genetic gain for each trait was estimated based on the family selection. The highest genetic gain was expected for the traits of branch angle, height and DBH because of the large phenotypic standard deviation and the high family heritability. The growth performance and branch characteristics were weighted by the magnitude of genetic variation and heritability. The weighted values were then subjected to estimate family breeding values. This family breeding value would be applied as a criterion in the genetic thinning of the seed orchard.

• Journal of radiological science and technology
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• v.32 no.1
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• pp.95-99
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• 2009

The purpose of this research is to seek SPAIR's reversal time (TI) which satisfies two conditions ; maintaining the suppression ability of fat tissue and simultaneously minimizing the inhomogeneity of fat tissue in T2 high-speed spin echo 3.0T magnetic resonance image (MRI) of the brain, and to compare SPAIR with STIR which is fat-suppression technique. The reversal times (TI) of SPAIR protocol are set to 1/2, 1/3, 1/6 and 1/12 of SPAIR TR (420 msec), namely 210 msec (8 people), 140 msec (26 people), 70 msec (26 people) and 35 msec (18 people) and STIR TI is set with 250 msec (26 people). With these parameter sets, we acquired the axis direction 104 images of the brain. In ROI ($50\;mm^2$) of output image, signal intensities of the fatty tissue, the muscular tissue, and the background were measured and the CNRs of fatty tissue and the muscular tissue were calculated. The inhomogeneity of the fatty tissue is SD/mean, where SD is the standard deviation and 'mean' is a average fatty tissue signal. Consequently, SPAIR TI is determined on either 1/3 or 1/6 of TR (420 ms) ; 140 ms or 70 ms. Because the difference of statistics in fat-suppression ability and inhomogeneity of fatty tissue is very small (p < 0.001), Selecting 140 ms seems to be better choice for the image quality. Meanwhile, Comparing SPAIR (TI : 140 ms) with STIR, the fat-suppression is not able to be considered statistically (p < 0.252), but the image quality is able to be considered statistically (p < 0.01). In conclusion, SPAIR is better than STIR in the image quality.