• Journal of the Korean Applied Science and Technology
• /
• v.38 no.1
• /
• pp.60-74
• /
• 2021

The purpose of this research was to determine the degree of awareness of early symptoms of stroke and myocardial infarction in adults diagnosed with hypertension and diabetes, and to analyze factors related to unawareness. Raw data from the 2017 Community Health Survey were utilized. A total 12,277 adults older than 40 years were included in analysis finally. The early symptom awareness rates of stroke and myocardial infarction were 53.6% and 46.8%, respectively. Logistic analysis was performed to assess factors associated with unawareness of early symptom of stroke and myocardial infarction. After correcting for socio-demographic variables, education for the management of hypertension, recognition of blood pressure level and HbA1c level were significantly related to stroke early symptom unawareness on multivariate analysis. (Education for the management of hypertension, recognition of blood pressure level and HbA1c level were significantly related to the unawareness of early stroke symptoms even after socio-demographic variables were corrected in multivariate analysis.) Recognition of blood pressure level and HbA1c level were also significantly associated with unawareness of early symptoms of myocardial infarction. Therefore, it is required to interventions and strategies to improve the level of awareness of early symptoms of cardio-cerebrovascular disease, such as accelerating promotion and strengthening education for high-risk groups who must manage both hypertension and diabetes.

• Korean Journal of Clinical Laboratory Science
• /
• v.54 no.2
• /
• pp.103-109
• /
• 2022

In December 2019, the coronavirus disease 2019 (COVID-19) caused by the virus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in China and spread rapidly around the world, infecting millions of people. Cases of COVID-19 infection were observed to lead to viral pneumonia. Thirty-five patients admitted to the Gyeonggi Medical Center, South Korea, between November 2020 to January 2021, were found to have been infected with the influenza virus A and B, which cause symptoms similar to COVID-19. The records of these patients and those of COVID-19 patients who visited the hospital for medical examination were compared. The study patients included thirty patients with COVID-19 and/or influenza, five of those with influenza alone. A group of 121 patients without infection was used as control. Patients with COVID-19 and influenza had significantly higher lactate dehydrogenase levels than the patients with COVID-19 alone. The erythrocyte sedimentation rate (ESR) was higher in patients with COVID-19 alone than in other groups. Significant clinical outliers were observed in the COVID-19 and influenza infection group compared with the COVID-19 alone group. These results are expected to play an important role in the analysis of the hematological data of infected patients and the comparison of simultaneous and single infection data to determine clinical symptoms and other signs. These results may also assist in the development of vaccines and treatments for COVID-19.

• Pediatric Infection and Vaccine
• /
• v.28 no.3
• /
• pp.144-148
• /
• 2021

Purpose: Nonpharmaceutical measures, such as social distancing, have resulted in unintended consequences, including a decrease in the incidence of childhood diseases. This study aimed to estimate the impact of social distancing on the incidence of febrile seizure (FS) in Korea using nationally representative data. Methods: We used claims data from the Health Insurance Review and Assessment Service, a single-payer database capturing >95% of the Korean population. The database included any inpatient encounter with a FS diagnosis from January 2010 to September 2020 for those aged 0-5 years old. We aggregated the monthly number of cases to estimate the incidence per 100,000 patient-years in 2020 (January 1 to September 30) for the same periods in 2010-2019. Results: The incidence of FS in 2020 ranged from 113 per 100,000 (95% confidence interval [CI], 108-118 per 100,000) in January to 27 per 100,000 (95% CI, 25-30 per 100,000) in September, whereas the average FS incidence in 2010-2019 ranged from 116 per 100,000 (95% CI, 112-121 per 100,000) in January to 101 per 100,000 (95% CI, 97-106 per 100,000) in September. Conclusions: The incidence of FS decreased by -38% in 2020, suggesting that social distancing contributed towards decreasing the incidence of FS.

• Journal of the Korean Society of Radiology
• /
• v.16 no.1
• /
• pp.25-34
• /
• 2022

Brain computed tomography (CT) is useful for brain lesion diagnosis, such as brain hemorrhage, due to non-invasive methodology, 3-dimensional image provision, low radiation dose. However, there has been numerous misdiagnosis owing to a lack of radiologist and heavy workload. Recently, object detection technologies based on artificial intelligence have been developed in order to overcome the limitations of traditional diagnosis. In this study, the applicability of a deep learning-based YOLOv5s model was evaluated for brain hemorrhage detection using brain CT images. Also, the effect of hyperparameters in the trained YOLOv5s model was analyzed. The YOLOv5s model consisted of backbone, neck and output modules. The trained model was able to detect a region of brain hemorrhage and provide the information of the region. The YOLOv5s model was trained with various activation functions, optimizer functions, loss functions and epochs, and the performance of the trained model was evaluated in terms of brain hemorrhage detection accuracy and training time. The results showed that the trained YOLOv5s model is able to provide a bounding box for a region of brain hemorrhage and the accuracy of the corresponding box. The performance of the YOLOv5s model was improved by using the mish activation function, the stochastic gradient descent (SGD) optimizer function and the completed intersection over union (CIoU) loss function. Also, the accuracy and training time of the YOLOv5s model increased with the number of epochs. Therefore, the YOLOv5s model is suitable for brain hemorrhage detection using brain CT images, and the performance of the model can be maximized by using appropriate hyperparameters.

• Journal of Nutrition and Health
• /
• v.56 no.4
• /
• pp.377-390
• /
• 2023

Purpose: Diabetes mellitus (DM) causes body fluid imbalance because of hyperglycemia, but there is a lack of research on the relationship between DM and body fluid imbalance in the Korean population. This study compared the differences in body fluid composition and dietary intake between individuals with type 2 DM (T2DM) and a normal control (NC) group without the disease. Methods: In this study, 36 subjects with T2DM and 21 without diabetes were divided into the T2DM and NC groups. The subjects were divided into four subgroups to assess differences in body fluid volume according to sex: men T2DM group (n = 24), men NC group (n = 9), women T2DM group (n = 12), and women NC group (n = 12). The body fluid composition was measured using bioelectrical impedance analysis, including intracellular water (ICW), extracellular water (ECW), total body water (TBW), ECW/ICW, and ECW/TBW. Nutrient intake was evaluated using their dietary records. Results: The results showed that the ECW/ICW and the ECW/TBW were significantly higher in the T2DM group compared to the NC group. Both men and women in the T2DM group showed significantly higher ECW/ICW and ECW/TBW than the respective NC group. The T2DM group had a higher carbohydrate, dietary fiber, vitamin A, vitamin C, sodium, and potassium intake per 1,000 kcal and lower total daily energy, fat, and cholesterol intake per 1,000 kcal than the NC group. Conclusion: These results suggest a positive association between T2DM and body fluid imbalance. This study can be used widely as basic data for the evaluation and diagnosis of diabetic complications in the future.

• The Korean Society of Law and Medicine
• /
• v.24 no.1
• /
• pp.43-65
• /
• 2023

The healthcare industry is a digital healthcare that combines technology based on the 4th Industrial Revolution, dealing with information on individual health and medical care, and is a fusion of health care services and medical science and technology. It is questionable whether digital healthcare according to the paradigm change can be discussed by the concept of medical practice under the existing Medical Act. There is no clear definition of the concept of medical practice in the Medical Service Act, but the concept is established through precedents. In addition, under the Medical Service Act, the subject of medical practice is limited to medical personnel. However, digital healthcare sometimes diagnoses and treats diseases using digital technology by medical personnel. On the other hand, what is possible by non-medical personnel is digital healthcare. This is because digital healthcare is understood as a concept that includes health care such as exercise, eating habits, and weight control. For this reason, if the concept of medical practice under the "Medical Act" on digital healthcare is included, it is subject to criminal punishment for "unlicensed medical practice" prescribed in Article 27 of the "Medical Act". In the health and medical industry, digital transformation and convergence with information and communication technology are rapidly progressing. As a result, there is a need to newly define it as 'digitalized medical practice' or 'information and communication technology (ICT)-based medical practice' separately from existing medical practices. The concept of medical practice has variability, not a fixed and invariable concept. However, in response to this demand, it is not an infinite expansion of the concept of medical practice, but a request to reset its scope. Therefore, the concept of medical practice should be legislated by reflecting the demand of consumers for the medical service system.

• Journal of the Korean Society for information Management
• /
• v.39 no.1
• /
• pp.91-117
• /
• 2022

The number of depressed patients in Korea and around the world is rapidly increasing every year. However, most of the mentally ill patients are not aware that they are suffering from the disease, so adequate treatment is not being performed. If depressive symptoms are neglected, it can lead to suicide, anxiety, and other psychological problems. Therefore, early detection and treatment of depression are very important in improving mental health. To improve this problem, this study presented a deep learning-based depression tendency model using Korean social media text. After collecting data from Naver KonwledgeiN, Naver Blog, Hidoc, and Twitter, DSM-5 major depressive disorder diagnosis criteria were used to classify and annotate classes according to the number of depressive symptoms. Afterwards, TF-IDF analysis and simultaneous word analysis were performed to examine the characteristics of each class of the corpus constructed. In addition, word embedding, dictionary-based sentiment analysis, and LDA topic modeling were performed to generate a depression tendency classification model using various text features. Through this, the embedded text, sentiment score, and topic number for each document were calculated and used as text features. As a result, it was confirmed that the highest accuracy rate of 83.28% was achieved when the depression tendency was classified based on the KorBERT algorithm by combining both the emotional score and the topic of the document with the embedded text. This study establishes a classification model for Korean depression trends with improved performance using various text features, and detects potential depressive patients early among Korean online community users, enabling rapid treatment and prevention, thereby enabling the mental health of Korean society. It is significant in that it can help in promotion.

• Journal of Life Science
• /
• v.33 no.6
• /
• pp.512-522
• /
• 2023

Cancer with unlimited cell growth is a leading cause of death globally. Various cancer treatments, including surgery, chemotherapy, radiation therapy, immunotherapy, and targeted therapy, can be applied alone or in combination depending on the cancer type and stage. New treatments with fewer side effects than previous cancer treatments are continually under development and in demand. Undifferentiated stem cells with unlimited cell growth are gradually changed via cellular differentiation to arrest cell growth. In this study, we reviewed the possibility of treating cancer by using cellular differentiation into the adipocytes in cancer cells. In previous in vitro studies, oral antidiabetic drugs of the thiazolidinedione (TDZ) class, such as rosiglitazone and pioglitazone, were induced into the adipocytes in various cancer cell lines via increased peroxisome proliferator-activated receptor-γ (PPAR γ) expression and glucose uptake, which is the key regulator of adipogenesis and the energy metabolism pathway. The differentiated adipogenic cancer cells treated with TDZ inhibited cell growth and had a less cellulotoxic effect. This adipogenic differentiation treatment suggests a possible chemotherapy option in cancer cells with high and abnormal glucose metabolism levels. However, the effects of the in vivo adipogenic differentiation treatment need to be thoroughly investigated in different types of stem and normal cells with other side effects.

• 한국체육학회지인문사회과학편
• /
• v.57 no.6
• /
• pp.263-272
• /
• 2018

This study examined the association of gait variability and physical fitness with frailty status in older women. In a cross-sectional design, 168 elderly women, aged 65 years and older (75.07±5.40 years), measured body composition, gait parameters gait variability, physical fitness variables, MMSE-DS and CES-D. Subjects were classified as robust, pre-frail, and frailty based on the Fried et al.(2001) criteria for frailty weight loss, exhaustion, low hand-grip strength, low gait speed, and physical inactivity. Logistic regression analyses were used to determine the odds ratio (ORs) and 95% confidence interval (CI) of frailty status for having gait variability and physical fitness levels. Compared to the robust group (OR=1), the frailty group had significantly higher ORs of having terminal double limb stance (OR=1.48, 95% CI=0.10-2.21, p=.049), step cadence (OR=2.06, 95%CI=1.20-3.43, p=.009) variability, and significantly lower ORs of having upper-strength (OR=0.49, 95%CI=0.31-0.77, p=.002) even after adjusting for age, education, comorbidity, K-IADL, MMSE-KC and CES-D score. The finding of this study suggested that terminal double limb stance, step cadence and upper body muscular strength were independent predictors of frailty.

• Convergence Security Journal
• /
• v.22 no.4
• /
• pp.85-97
• /
• 2022

With the development of big data technology, data is rapidly entering a hyperconnected intelligent society that accelerates innovative growth in all industries. The convergence industry, which holds and utilizes various high-quality data, is becoming a new growth engine, and big data is fused to various traditional industries. In particular, in the medical field, structured data such as electronic medical record data and unstructured medical data such as CT and MRI are used together to increase the accuracy of disease prediction and diagnosis. Currently, the importance and size of unstructured data are increasing day by day in the medical industry, but conventional data security technologies and policies are structured data-oriented, and considerations for the security and utilization of unstructured data are insufficient. In order for medical treatment using big data to be activated in the future, data diversity and security must be internalized and organically linked at the stage of data construction, distribution, and utilization. In this paper, the current status of domestic and foreign data security systems and technologies is analyzed. After that, it is proposed to add unstructured data-centered de-identification technology to the guidelines for unstructured data and technology application cases in the industry so that unstructured data can be actively used in the medical field, and to establish standards for judging personal information for unstructured data. Furthermore, an object feature-based identification ID that can be used for unstructured data without infringing on personal information is proposed.