• Journal of Technology Innovation
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• v.30 no.4
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• pp.57-82
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• 2022

As the strategy to support SMEs' innovation has shifted to induce market-oriented innovation, the Korean government increasingly invests in grant-type R&D projects proposed by SME firms. This study reveals the characteristics of grant-type R&D projects proposed by SME firms through a transition of national R&D strategy and demand-pull innovation perspectives. This study also examines the differences between grant-type R&D projects proposed by firms and R&D projects led by the government through logit-analysis and propensity score matching methods. As a result, we found that a national R&D project for SMEs yields better innovation performance when the project is proposed by a company than led by the government.

• Korean Journal of Clinical Pharmacy
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• v.32 no.1
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• pp.37-46
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• 2022

Background: Non-valvular atrial fibrillation (NVAF) is associated with ischemic stroke risk in the aging population. Observational studies have indicated beneficial effects of direct-acting oral anticoagulant (DOAC) against ischemic stroke compared to warfarin. This study aimed to investigate ischemic stroke incidence and bleeding risk in patients on DOAC therapy. Methods: Using the database of Korean Health Insurance Review and Assessment-Aged Patient Sample 2015, we conducted a retrospective cohort study. Study subjects with NVAF diagnosis and prescribed anticoagulants were enrolled. Propensity score (PS) matching by age, sex, comorbidities, and medications were used. The clinical outcomes were major adverse cerebro-cardiovascular events (MACCEs, ischemic stroke/systemic embolism, myocardial infarction, cardiac death) and bleeding events. A cox proportional hazard model analysis was performed to compare the outcomes with hazard ratio (HR) and 95% confidence interval (CI). Results: Total 4,773 elderly patients with NVAF were initially included. Four PS-matched groups including rivaroxaban vs. warfarin-only (n=1,079), dabigatran vs. warfarin-only (n=721), rivaroxaban vs. dabigatran (n=721), and switchers of warfarin to rivaroxaban vs. warfarin-only (n=287) were analyzed. Every group showed statistically similar results of MACCEs and bleeding events, except for the group of rivaroxaban vs. dabigatran. Rivaroxaban users showed higher risks of bleeding events than dabigatran users (HR 2.25, 95% CI 1.01-4.99). Conclusion: In the elderly patients with NVAF, efficacy and safety outcomes among oral anticoagulants including DOACs and warfarin were similar, while rivaroxaban are more likely to have higher bleeding risks than dabigatran. Further research using large size sample is needed.

• Journal of Gastric Cancer
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• v.24 no.2
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• pp.199-209
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• 2024

Purpose: Long-term outcomes of patients with positive lateral margins (pLMs) after endoscopic submucosal dissection (ESD) for early gastric cancer (EGC). This study aimed to evaluate the remnant cancer and survival rates of patients with pLMs compared with those who underwent curative resection. Materials and Methods: A retrospective analysis was performed on consecutive patients with pLMs as the only non-curative factor of expanded indication who underwent ESD for EGC with a follow-up duration of 5 years or more. The rates of remnant cancer, recurrence, and survival were analyzed and compared to those of control patients who underwent curative resection by propensity score matching. Results: Among 3,515 patients treated with ESD between 2005 and 2018, 123 non-curative EGCs were retrospectively analyzed. A total of 108 patients were followed up without endoscopic or surgical resection for 8.2 years. The control group was matched in a 1:1 ratio with patients with EGC who underwent curative resection after ESD. The observation group with pLMs had a higher incidence of remnant cancer (25.9%; 28/108) compared to that in the curative resection group (0/108; P=0.000). The remaining tumors were treated with surgical or endoscopic resection, and no additional recurrences were observed. The overall survival analysis demonstrated no significant difference between the observation and curative resection groups (P=0.577). Conclusions: No difference was observed in the overall survival rate between observation and curative resection groups. Therefore, observation may be a possible option for incomplete ESD with pLMs if continuous follow-up is performed.

• Asia-Pacific Journal of Business
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• v.14 no.4
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• pp.191-204
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• 2023

Purpose - The purpose of this study was to analyse the relationship between R&D tax support policy and firm innovation activity using data on firms engaged in 12 national strategic technology sectors. Design/methodology/approach - This study collected survey data from 664 companies engaged in national strategic technologies. The data were then analysed using the Propensity Score Matching (PSM) analysis. Findings - First, corporate R&D tax support had a statistically significant positive (+) relationship with firm innovation performance. Second, there was a statistically significant positive (+) relationship with incremental innovation, but there was no statistical significance with radical innovation. Third, there was a statistically significant positive (+) relationship with the firm's first innovation, but there was no statistical significance with the world's first innovation. Fourth, there was a statistically significant positive (+) relationship with the number of R&D projects of a firm. Finally, there was a statistically significant positive (+) relationship with a firm's open innovation. Research implications or Originality - First, in terms of policy effectiveness, the government needs to consider promoting R&D tax support policies in areas where R&D competition is fierce. For private companies engaged in the 12 national strategic technology fields, the R&D tax support policy is working in the direction of promoting corporate innovation activities, and this positive policy effect is likely to be effective in areas where R&D competition is fierce. Second, if the government wants to improve the quality of corporate innovation activities through R&D tax support policies, it needs to provide incentives higher than the current level.

• Journal of Korean Medicine Rehabilitation
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• v.31 no.4
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• pp.105-116
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• 2021

Objectives Lumbar herniated intervertebral disc (L-HIVD) is common disease in which Western-Korean collaborative treatment is performed in Korea. This study aimed to analyze Western-Korean collaborative treatment utilization of Korean patients with L-HIVD using Health Insurance Review & Assessment Service's Patients Sample Data. Methods This study used the Health Insurance Review & Assessment Service-National Patient Sample (HIRA-NPS) in 2018. Claim data of L-HIVD patients were extracted. The claim data were rebuilt with the operational concept of 'episode of care' and divided into Korean medicine episode group (KM), Western medicine episode group (WM) and collaborative treatment episode group (CT). General characteristics, medical expenses and healthcare utilization were analyzed. In addition, the difference of average visit day and average medical expenses between non-collaborative group (KM plus WM) and CT were analyzed by the propensity score matching method. Results A Total of 64,333 patients and 365,745 claims were extracted. The number of episodes of WM, KM and CT was 69,383 (92.97%), 3,903 (5.23%), and 1,341 (1.80%) respectively. The frequency of collaborative treatment episode was higher in women and the age of 50s. The most frequently described treatment in CT was acupuncture therapy. As a result of the propensity score matching, the number of visit days and medical expenses in the collaborative treatment group was higher than in the non-collaborative group. Conclusions The analysis of healthcare utilization of Korean-Western collaborative treatment may be used as basic data for establishing medical policies and systematic collaborative treatment model in the future.

• Journal of the Korean Applied Science and Technology
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• v.40 no.4
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• pp.685-698
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• 2023

This study aimed to identify binge-eating behavior and food addiction in Korean population and to determine their associations with obesity, eating behaviors, mental health and cognitive characteristics. We collected clinical questionnaire scores related to eating problems (e.g. binge eating, food addiction, food cravings), mental health (e.g. depression), and cognitive functions (e.g. impulsivity, emotion regulation) in 257 Korean adults in the normal and the obese weight ranges. Binge-eating and food addiction were most frequent in obese women (binge-eating: 46.6%, food addiction: 29.3%) when we divided the participants into 4 groups depending on gender and obesity status. The independence test using the data with propensity score matching confirmed that binge-eating and food addiction were more prevalent in obese individuals. Finally, we constructed the logistic regression models using forward selection method to evaluate the influence of various clinical questionnaire scores on binge-eating and food addiction respectively. Binge-eating was significantly associated with the clinical scales of eating disorders, food craving, state anxiety, and emotion regulation (cognitive reappraisal) as well as food addiction. Food addiction demonstrated the significant effect of food craving, binge-eating, the interaction of obesity and age, and years of education. In conclusion, we found that binge-eating and food addiction are much more frequent in females and obese individuals. Both binge-eating and food addiction commonly involved eating problems (e.g. food craving), but there was difference in mental health and cognitive risk factors. Therefore, it is required to distinguish food addiction from binge-eating and investigate intrinsic and environmental risk factors for each pathology.

• Health Policy and Management
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• v.34 no.2
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• pp.129-140
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• 2024

Background: The mental health issues caused by trauma can manifest differently depending on the characteristics of the traumatic event. Particularly, individuals who have experienced sexual trauma are known to have more negative mental health outcomes compared to those who have experienced non-sexual trauma. The mental health issues of individuals who have experienced sexual trauma are severe, and new forms of threats, such as digital sexual crimes, are emerging. This study aimed to investigate whether the type of traumatic event, particularly focusing on sexual trauma events, contributes to differences in mental health outcomes and to identify factors influencing suicidal ideation and potential post-traumatic stress disorder (PTSD) risk. Methods: Based on an online survey conducted nationwide among adults aged 20 to 50, participants were categorized based on the type of trauma they experienced (sexual trauma events and non-sexual trauma events). The study conducted propensity score matching (PSM) using demographic factors (sex, age group, subjective economic status, and marital status) and resilience protective factors (cognition of recoverability, social support, and protection experiences in childhood) as control variables, excluding the experience of sexual trauma events, to investigate their potential impact on mental health (suicidal ideation and potential PTSD risk). Subsequently, binary logistic regression analysis was conducted to identify factors influencing mental health. Results: Even after PSM, individuals who experienced sexual trauma exhibited more negative outcomes in terms of suicidal ideation and potential PTSD risk compared to those who experienced non-sexual trauma. The results of binary logistic regression analysis showed that sexual trauma survivors were 1.9 times more likely to have suicidal thoughts (odds ratio [OR], 1.911) and 2.5 times more likely to have a potential PTSD risk (OR, 2.472). Furthermore, as resilience protective factors became more negative, the likelihood of suicidal ideation and potential PTSD risk increased. Conclusion: This study emphasizes the importance of understanding and supporting individuals who have experienced sexual trauma, highlighting the necessity for strategies aimed at mitigating suicidal ideation and potential PTSD risk among sexual trauma survivors, while also facilitating recovery through the promotion of resilience protective factors.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.43-62
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• 2019

At one time, the anomaly detection sector dominated the method of determining whether there was an abnormality based on the statistics derived from specific data. This methodology was possible because the dimension of the data was simple in the past, so the classical statistical method could work effectively. However, as the characteristics of data have changed complexly in the era of big data, it has become more difficult to accurately analyze and predict the data that occurs throughout the industry in the conventional way. Therefore, SVM and Decision Tree based supervised learning algorithms were used. However, there is peculiarity that supervised learning based model can only accurately predict the test data, when the number of classes is equal to the number of normal classes and most of the data generated in the industry has unbalanced data class. Therefore, the predicted results are not always valid when supervised learning model is applied. In order to overcome these drawbacks, many studies now use the unsupervised learning-based model that is not influenced by class distribution, such as autoencoder or generative adversarial networks. In this paper, we propose a method to detect anomalies using generative adversarial networks. AnoGAN, introduced in the study of Thomas et al (2017), is a classification model that performs abnormal detection of medical images. It was composed of a Convolution Neural Net and was used in the field of detection. On the other hand, sequencing data abnormality detection using generative adversarial network is a lack of research papers compared to image data. Of course, in Li et al (2018), a study by Li et al (LSTM), a type of recurrent neural network, has proposed a model to classify the abnormities of numerical sequence data, but it has not been used for categorical sequence data, as well as feature matching method applied by salans et al.(2016). So it suggests that there are a number of studies to be tried on in the ideal classification of sequence data through a generative adversarial Network. In order to learn the sequence data, the structure of the generative adversarial networks is composed of LSTM, and the 2 stacked-LSTM of the generator is composed of 32-dim hidden unit layers and 64-dim hidden unit layers. The LSTM of the discriminator consists of 64-dim hidden unit layer were used. In the process of deriving abnormal scores from existing paper of Anomaly Detection for Sequence data, entropy values of probability of actual data are used in the process of deriving abnormal scores. but in this paper, as mentioned earlier, abnormal scores have been derived by using feature matching techniques. In addition, the process of optimizing latent variables was designed with LSTM to improve model performance. The modified form of generative adversarial model was more accurate in all experiments than the autoencoder in terms of precision and was approximately 7% higher in accuracy. In terms of Robustness, Generative adversarial networks also performed better than autoencoder. Because generative adversarial networks can learn data distribution from real categorical sequence data, Unaffected by a single normal data. But autoencoder is not. Result of Robustness test showed that he accuracy of the autocoder was 92%, the accuracy of the hostile neural network was 96%, and in terms of sensitivity, the autocoder was 40% and the hostile neural network was 51%. In this paper, experiments have also been conducted to show how much performance changes due to differences in the optimization structure of potential variables. As a result, the level of 1% was improved in terms of sensitivity. These results suggest that it presented a new perspective on optimizing latent variable that were relatively insignificant.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.25-38
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• 2019

Selecting high-quality information that meets the interests and needs of users among the overflowing contents is becoming more important as the generation continues. In the flood of information, efforts to reflect the intention of the user in the search result better are being tried, rather than recognizing the information request as a simple string. Also, large IT companies such as Google and Microsoft focus on developing knowledge-based technologies including search engines which provide users with satisfaction and convenience. Especially, the finance is one of the fields expected to have the usefulness and potential of text data analysis because it's constantly generating new information, and the earlier the information is, the more valuable it is. Automatic knowledge extraction can be effective in areas where information flow is vast, such as financial sector, and new information continues to emerge. However, there are several practical difficulties faced by automatic knowledge extraction. First, there are difficulties in making corpus from different fields with same algorithm, and it is difficult to extract good quality triple. Second, it becomes more difficult to produce labeled text data by people if the extent and scope of knowledge increases and patterns are constantly updated. Third, performance evaluation is difficult due to the characteristics of unsupervised learning. Finally, problem definition for automatic knowledge extraction is not easy because of ambiguous conceptual characteristics of knowledge. So, in order to overcome limits described above and improve the semantic performance of stock-related information searching, this study attempts to extract the knowledge entity by using neural tensor network and evaluate the performance of them. Different from other references, the purpose of this study is to extract knowledge entity which is related to individual stock items. Various but relatively simple data processing methods are applied in the presented model to solve the problems of previous researches and to enhance the effectiveness of the model. From these processes, this study has the following three significances. First, A practical and simple automatic knowledge extraction method that can be applied. Second, the possibility of performance evaluation is presented through simple problem definition. Finally, the expressiveness of the knowledge increased by generating input data on a sentence basis without complex morphological analysis. The results of the empirical analysis and objective performance evaluation method are also presented. The empirical study to confirm the usefulness of the presented model, experts' reports about individual 30 stocks which are top 30 items based on frequency of publication from May 30, 2017 to May 21, 2018 are used. the total number of reports are 5,600, and 3,074 reports, which accounts about 55% of the total, is designated as a training set, and other 45% of reports are designated as a testing set. Before constructing the model, all reports of a training set are classified by stocks, and their entities are extracted using named entity recognition tool which is the KKMA. for each stocks, top 100 entities based on appearance frequency are selected, and become vectorized using one-hot encoding. After that, by using neural tensor network, the same number of score functions as stocks are trained. Thus, if a new entity from a testing set appears, we can try to calculate the score by putting it into every single score function, and the stock of the function with the highest score is predicted as the related item with the entity. To evaluate presented models, we confirm prediction power and determining whether the score functions are well constructed by calculating hit ratio for all reports of testing set. As a result of the empirical study, the presented model shows 69.3% hit accuracy for testing set which consists of 2,526 reports. this hit ratio is meaningfully high despite of some constraints for conducting research. Looking at the prediction performance of the model for each stocks, only 3 stocks, which are LG ELECTRONICS, KiaMtr, and Mando, show extremely low performance than average. this result maybe due to the interference effect with other similar items and generation of new knowledge. In this paper, we propose a methodology to find out key entities or their combinations which are necessary to search related information in accordance with the user's investment intention. Graph data is generated by using only the named entity recognition tool and applied to the neural tensor network without learning corpus or word vectors for the field. From the empirical test, we confirm the effectiveness of the presented model as described above. However, there also exist some limits and things to complement. Representatively, the phenomenon that the model performance is especially bad for only some stocks shows the need for further researches. Finally, through the empirical study, we confirmed that the learning method presented in this study can be used for the purpose of matching the new text information semantically with the related stocks.

• KDI Journal of Economic Policy
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• v.32 no.2
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• pp.55-92
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• 2010

In terms of labor market accomplishments, such as income, size of the company, and the matching quality between one's job and college major (specialization), a very large discrepancy is observed between the graduates from colleges located in Seoul and those outside Seoul. But, when the department average score of the Scholastic Aptitude Test (SAT) at the time of college entrance is controlled for, the discrepancy is found to be reduced to a considerable degree. In the case of wage gap, at least two third can be explained by the SAT score gap. The remaining wage gap seems to reflect the characteristics of workplace. In other words, graduates with high SAT scores enter colleges located in Seoul and thus tend to find better jobs leading to earning differences. This result that confirms the importance of aptitude test scores suggests that in the labor market, one of the major reasons behind a lower accomplishment of the graduate from local colleges is due to a lower competitiveness of local colleges in attracting the brightest students. But, this should not be viewed as only an internal problem of local colleges. This is because the growth of local economies tends to haul the advancement of local colleges in that area rather than being the other way around. The agglomeration effect in Seoul where headquarters of large corporations and financial institutions gather is the factor that has elevated the status of colleges located in Seoul since this provides highly preferred job choices of graduates. When the competitiveness of college is significantly influenced by exogenous factors, such as the vicinity to Seoul, the effort being made by colleges alone would not be enough to improve the situation. However, the central government, too, is not in the position to carry out countermeasure policies for such problems. The regional development strategy boosted through supportive policies for local colleges, such as financial support, is not based on the persuasive and empirical grounds. It is true that college education is universal and that the government''s intervention in assisting local colleges to secure basic conditions, such as tenure faculty and adequate facilities is necessary. However, the way of intervention should not be a support-only type. In order to improve the efficiency and effect of financial support, restructuring programs, including the merger and integration of insolvent colleges, should be underway prior to providing support. In addition, when the policy is focused on education recipients-local college students, and not on education providers-local colleges, the importance of regional gap in compulsory education (elementary and junior high schools) turns out to be much important as the gap between metropolitan area colleges and local colleges. Considering the educational gap before college entrance shown from the discrepancies of aptitude test scores among different regions, the imbalance between regions in terms of human resources is apparently derived from compulsory education, and not from college education. Therefore, there is a need to double the policy efforts to reduce the educational gap among different regions. In addition, given the current situation where it is difficult to find appropriate ex post facto policy measures to solve the problem of income gap between the graduates from metropolitan colleges and local colleges, it can be said that improving the environment for compulsory education in local areas is a growing necessity for bridging the educational gap among different regions.