• Annual Conference of KIPS
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• 2023.05a
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• pp.748-750
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• 2023

Facial expression recognition technology's rapid growth and development have garnered significant attention in recent years. This technology holds immense potential for various applications, making it crucial to stay up-to-date with the latest trends and advancements. Simultaneously, it is essential to identify and address the challenges that impede the technology's progress. Motivated by these factors, this study aims to understand the latest trends, future directions, and challenges in facial expression recognition technology by utilizing text mining to analyze papers published between 2020 and 2023. Our research focuses on discerning which aspects of these papers provide valuable insights into the field's recent developments and issues. By doing so, we aim to present the information in an accessible and engaging manner for readers, enabling them to understand the current state and future potential of facial expression recognition technology. Ultimately, our study seeks to contribute to the ongoing dialogue and facilitate further advancements in this rapidly evolving field.

• Korean Medical Education Review
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• v.19 no.3
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• pp.158-168
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• 2017

There is a more urgent call for educational methods of machine learning in medical education, and therefore, new approaches of teaching and researching machine learning in medicine are needed. This paper presents a case using machine learning through text analysis. Topic modeling of news articles with the keyword 'asbestos' were examined. Two hypotheses were tested using this method, and the process of machine learning of texts is illustrated through this example. Using an automated text analysis method, all the news articles published from January 1, 1990 to November 15, 2016 in South Korea which included 'asbestos' in the title and the body were collected by web scraping. Differences in topics were analyzed by structured topic modelling (STM) and compared by press companies and periods. More articles were found in liberal media outlets. Differences were found in the number and types of topics in the articles according to the partisanship and period. STM showed that the conservative press views asbestos as a personal problem, while the progressive press views asbestos as a social problem. A divergence in the perspective for emphasizing the issues of asbestos between the conservative press and progressive press was also found. Social perspective influences the main topics of news stories. Thus, the patients' uneasiness and pain are not presented by both sources of media. In addition, topics differ between news media sources based on partisanship, and therefore cause divergence in readers' framing. The method of text analysis and its strengths and weaknesses are explained, and an application for the teaching and researching of machine learning in medical education using the methodology of text analysis is considered. An educational method of machine learning in medical education is urgent for future generations.

• The Korean journal of internal medicine
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• v.39 no.2
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• pp.338-346
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• 2024

Background/Aims: Systemic lupus erythematosus (SLE) responder index (SRI)-4 response has been achieved with belimumab treatment in patients with moderate disease activity in cornerstone clinical trials and following studies. However, most studies involved patients treated with a mean prednisolone-equivalent dose of approximately 10 mg/d and focused on the steroid-sparing effect of belimumab. We aimed to identify the effect of belimumab in patients with mild-to-moderate SLE who were treated with low-dose or no corticosteroids. Methods: We retrospectively reviewed the electronic medical records of patients treated with belimumab for at least 6 months between May 2021 and June 2022. The primary endpoint was SRI-4 response at 6 months. Results: Thirty-one patients were included (13 low dose- and 18 steroid non-users). The mean age was 39.2 ± 11.4 years, and 90.3% of patients were female. The baseline Safety of Estrogens in Lupus Erythematosus National Assessment-Systemic Lupus Erythematosus Disease Activity Index (SELENA-SLEDAI) score was 6.0 (4.0-9.0). The primary endpoint was achieved in 32.3% (10/31) of patients. Significant improvements in anemia, C4 levels, and SELENA-SLEDAI score were observed during treatment. Univariate analysis showed that the baseline SELENA-SLEDAI and arthritis were significantly associated with SRI-4 response at 6 months, and only the SELENA-SLEDAI remained significant (p = 0.014) in multivariate analysis. Conclusions: This cohort study is the first to report the efficacy of belimumab after minimizing the effect of corticosteroids. Belimumab showed efficacy in improving the SELENA-SLEDAI score, anemia, and low C4 in patients who did not receive corticosteroids or received only low doses.

• Journal of Service Research and Studies
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• v.9 no.3
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• pp.87-99
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• 2019

Healthcare and data science are often linked through finances as the industry attempts to reduce its expenses with the help of large amounts of data. Data science and medicine are rapidly developing, and it is important that they advance together. Data science is a driving force in transition of healthcare systems from treatment-oriented to preventive care in healthcare 3.0 era. It enables customized precision-based medicine that current healthcare systems cannot facilitate, and discovers more cost-effective treatment. Currently, healthcare big data is in the reality of medical institution, public health, medical academia, pharmaceutical sector as well as insurance agency. With this motivation, the medical college of Soonchunhyang university has performed a 'healthcare data science initiative(HDSI)' since 2014. Most of domestic HDSI programs focus on short-term contents such as mentoring and sharing cases for data science. Therefore, it is difficult to provide education tailored to the level of skills and job competency required at the practical site. Soonchunhyang HDSI implemented specialized strategies for improving resilience and response to changes in the IT education of current healthcare with the emphasis on the need for systematic activation of the practical HDSI. The HDSI has been performed as a part of on industry-academic link program in CK-1. Through quantitative and qualitative analysis, this paper discussed the HDSI process, performance, achievement, and implications.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.113-125
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• 2013

Every company wants to know customer's requirement and makes an effort to meet them. Cause that, communication between customer and company became core competition of business and that important is increasing continuously. There are several strategies to find customer's needs, but VOC (Voice of customer) is one of most powerful communication tools and VOC gathering by several channels as telephone, post, e-mail, website and so on is so meaningful. So, almost company is gathering VOC and operating VOC system. VOC is important not only to business organization but also public organization such as government, education institute, and medical center that should drive up public service quality and customer satisfaction. Accordingly, they make a VOC gathering and analyzing System and then use for making a new product and service, and upgrade. In recent years, innovations in internet and ICT have made diverse channels such as SNS, mobile, website and call-center to collect VOC data. Although a lot of VOC data is collected through diverse channel, the proper utilization is still difficult. It is because the VOC data is made of very emotional contents by voice or text of informal style and the volume of the VOC data are so big. These unstructured big data make a difficult to store and analyze for use by human. So that, the organization need to automatic collecting, storing, classifying and analyzing system for unstructured big VOC data. This study propose an intelligent VOC analyzing system based on opinion mining to classify the unstructured VOC data automatically and determine the polarity as well as the type of VOC. And then, the basis of the VOC opinion analyzing system, called domain-oriented sentiment dictionary is created and corresponding stages are presented in detail. The experiment is conducted with 4,300 VOC data collected from a medical website to measure the effectiveness of the proposed system and utilized them to develop the sensitive data dictionary by determining the special sentiment vocabulary and their polarity value in a medical domain. Through the experiment, it comes out that positive terms such as "칭찬, 친절함, 감사, 무사히, 잘해, 감동, 미소" have high positive opinion value, and negative terms such as "퉁명, 뭡니까, 말하더군요, 무시하는" have strong negative opinion. These terms are in general use and the experiment result seems to be a high probability of opinion polarity. Furthermore, the accuracy of proposed VOC classification model has been compared and the highest classification accuracy of 77.8% is conformed at threshold with -0.50 of opinion classification of VOC. Through the proposed intelligent VOC analyzing system, the real time opinion classification and response priority of VOC can be predicted. Ultimately the positive effectiveness is expected to catch the customer complains at early stage and deal with it quickly with the lower number of staff to operate the VOC system. It can be made available human resource and time of customer service part. Above all, this study is new try to automatic analyzing the unstructured VOC data using opinion mining, and shows that the system could be used as variable to classify the positive or negative polarity of VOC opinion. It is expected to suggest practical framework of the VOC analysis to diverse use and the model can be used as real VOC analyzing system if it is implemented as system. Despite experiment results and expectation, this study has several limits. First of all, the sample data is only collected from a hospital web-site. It means that the sentimental dictionary made by sample data can be lean too much towards on that hospital and web-site. Therefore, next research has to take several channels such as call-center and SNS, and other domain like government, financial company, and education institute.

• Journal of Advanced Navigation Technology
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• v.25 no.1
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• pp.96-101
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• 2021

This paper presents a machine learning model that predicts stroke risks in atrial fibrillation patients using public big data. As the training data, 68 independent variables including demographic, medical history, health examination were collected from the Korean National Health Insurance Service. To predict stroke incidence in patients with atrial fibrillation, we applied deep neural network. We firstly verify the performance of conventional statistical models (CHADS2, CHA2DS2-VASc). Then we compared proposed model with the statistical models for various hyperparameters. Accuracy and area under the receiver operating characteristic (AUROC) were mainly used as indicators for performance evaluation. As a result, the model using batch normalization showed the highest performance, which recorded better performance than the statistical model.

• Journal of the Korea Convergence Society
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• v.13 no.5
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• pp.365-372
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• 2022

South Korea is entering an aging society, and since the musculoskeletal system directly affects elders' daily life, muscle exercise and flexibility are required. In particular, yoga relaxes the mind and the body and heightens stress coping ability. To investigate keywords about yoga, news articles provided by BIGKinds, a news analysis system, was applied to collect articles from January 1, 2019, to December 31, 2021, and an analysis was conducted about the monthly keywords and the relationship followed by the weighted degree. Based on the research findings, first, it showed that there is high interest in yoga during the spring and autumn seasons. Second, yoga is offered in non-contact methods nowadays, and various social network services are applied for the operation. Third, there was high public attention to articles on yoga instructors and trainers, and this revealed the importance and interest in online coaching. It is anticipated to apply it for the development of yoga workout programs and base data to develop sports for all.

• Journal of Home Health Care Nursing
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• v.30 no.2
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• pp.141-154
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• 2023

Purpose: This study aimed to explore the social perception of visiting nursing before and after coronavirus disease (COVID-19). Methods: This survey-based study used online big data for comparative analysis by classifying the keywords related to visiting nursing searched on domestic portal sites before and after COVID-19. Results: According to the results of analyzing the Intertopic Distance Map based on Latent Dirichlet Allocation in this study, four topics were extracted, two each before and after COVID-19. The first topic before the COVID-19 period was termed "the expansion of visiting nursing subjects and services visiting nursing," while the second was termed "visiting nursing," which is related to customized welfare. The first topic after the COVID-19 period was termed "the suspension and resumption of visiting nursing services," while the second was "the development of a non-face-to-face home visit healthcare system". Conclusion: The results of this study can be used as useful reference data to contribute to future medical service delivery system reform policies starting at the end of COVID-19 and the revitalization of community care for visiting nursing.

• Journal of Society of Preventive Korean Medicine
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• v.3 no.2
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• pp.25-53
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• 1999

This study was conducted to provide basic data for policy of Oriental medicine by analyzing community residents' consciousness of taking herb medicine and its related factors. Data were collected from 1478 residents from March 2, 1999 to May 31, 1999. The results of this study are summarized as follows. 1. According to general characteristics of the subjects, 52.3% of the subjects was 'female'; 25.0% 'fifties of age'; 21.4% 'forties of age'; 20.9% 'thirties of ages'; 69.1% 'married'; 60.1% 'resident in a big city'; 12.1% 'residents in a small town or village'; 39.0% 'highschool graduate'; 35.9% 'above college graduate'; 23.4% 'housewife'; 23.4% 'professional' 34.1% 'Buddhist'; 81.1% 'middle class'. 2. The rate of experience of taking herb medicine was 85.2%(88.2% of 'male'; 82.5% of 'female'). It appeared to be significantly higher in the groups of 'the married', 'housewife', and 'Buddhist'. As the age increased, so the rate of experience of taking herb medicine was significantly high. 3. In case of purpose of taking herb medicine, taking herb medicine as a restorative(66.8%) was much higher than taking it as a curative medicine. Taking herb medicine as a curative medicine appeared to be significantly higher in the groups of 'male', 'thirties of age', 'resident in a town or village', 'above college graduate', 'professional technician', 'Christian', and 'the upper class'. 4. 52.1% of the respondents satisfied with the effect of herb medicine. The groups of 'male', 'older age', 'residents in a big city', 'insurant in company', and 'the employed' showed significantly high rate in satisfying with herb medicine than the other groups. 5. According to the reason for preferring herb medicine, 36.7% of the respondents preferred herb medicine because the herb medicine was effective, while 27.8% preferred it because its side effect was low. 16.7% preferred it. because persons around them recommended it. The preference for the herb medicine displayed significantly higher rate in the groups 'sixties of age', 'the unmarried', 'resident in a big city', 'office clerk', and 'the lower class'. 6. 42.6% of the respondents did not want to take the herb medicine because the price of the herb medicine was high. Also 20.6% of the respondents did not want to take herb medicine because it is uneasy to take herb medicine. 15.8% did not want to take it because certain foods should not be taken during the period of taking it. 9.4% did not want to take it because it tasted bitter. 7. In case of opinions on side effects of herb medicine, 40.8% of the respondents thinks that herb medicine is free from side effects, while 37.5% thinks that it causes side effects. There were significant difference in the opinions on side effects by sex, age, marital status, resident area, education level, occupation, and type of health insurance. 8. 60.7% of the respondents thinks the price of herb medicine is not resonable, while only 10.9% thinks it is resonable. 9. 14.2% of the respondents thinks health foods which contain herbs are good, while 16.8% thinks it is bad. 76.7% thinks that medicinal herbs in packages must be included in health insurance coverage, while only 3.0% thinks it needs not be included in health insurance coverage. 10. 45.2% of the respondents uses packs of decocted herbs although they think the packs of decocted herb are a little low effective because decocting herbs in home is bothersome. 45.2% uses packs of decocted herbs because they are convenient, being not related to the effect. 7.6% takes medicinal herbs after decocting them in a clay pot because they think the packs of decocted herbs have low effect. 11. According to the level of satisfaction with Oriental medical care, the respondents marked $3.47{\pm}0.64$ points on the base of 5 points. It was significantly higher in the groups of 'male', 'the married, resident in a big city', 'highschool graduate', 'the unemployed', 'office clerk', 'growing up in a big city', 'insurant in region', and 'the middle class'. 12. According to the result of a regression analysis of factors influencing preference for herb medicine, the factors displayed significant difference by sex, age, education level, health status, and times of receiving Oriental medical care. As shown in the above results, the community residents satisfy with the effect of herb medicine. Therefore, the method of taking herb medicine without difficulty must be devised. The medicinal herbs in packages need to be included in health insurance coverage and resonable price of herb medicine must be set. Also, education program for community residents must be developed in order to provide right information in herb medicine. Therefore, related public authority, associations, and professionals must make efforts, forming organic cooperative system.

• The Korean Journal of Pain
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• v.33 no.2
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• pp.153-165
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• 2020

Background: Well-validated risk prediction models help to identify individuals at high risk of diseases and suggest preventive measures. A recent systematic review reported lack of validated prediction models for low back pain (LBP). We aimed to develop prediction models to estimate the 8-year risk of developing LBP and its recurrence. Methods: A population based prospective cohort study using data from 435,968 participants in the National Health Insurance Service-National Sample Cohort enrolled from 2002 to 2010. We used Cox proportional hazards models. Results: During median follow-up period of 8.4 years, there were 143,396 (32.9%) first onset LBP cases. The prediction model of first onset consisted of age, sex, income grade, alcohol consumption, physical exercise, body mass index (BMI), total cholesterol, blood pressure, and medical history of diseases. The model of 5-year recurrence risk was comprised of age, sex, income grade, BMI, length of prescription, and medical history of diseases. The Harrell's C-statistic was 0.812 (95% confidence interval [CI], 0.804-0.820) and 0.916 (95% CI, 0.907-0.924) in validation cohorts of LBP onset and recurrence models, respectively. Age, disc degeneration, and sex conferred the highest risk points for onset, whereas age, spondylolisthesis, and disc degeneration conferred the highest risk for recurrence. Conclusions: LBP risk prediction models and simplified risk scores have been developed and validated using data from general medical practice. This study also offers an opportunity for external validation and updating of the models by incorporating other risk predictors in other settings, especially in this era of precision medicine.