• Clinical and Experimental Pediatrics
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• v.50 no.4
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• pp.369-375
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• 2007

Purpose : There have been numerous researches on urine ${\beta}_2$-microglobulin (${\beta}_2$-M) concerned with primary nephrotic syndrome and other glomerular diseases, but not much has been done in relation to pediatric age groups. Thus, our hospital decided to study the relations between the analysis of the test results we have conducted on pediatric patients and renal functions. Methods : Retrospective data analysis was done to 102 patients of ages 0 to 4 with renal diseases with symptoms such as hematuria, edema, and proteinuria who were admitted to Chung-Ang Yongsan Hospital and who participated in 24-hour urine and urine ${\beta}_2$-M excretion test between January of 2003 and January of 2006. Each disease was differentiated as independent variables, and the statistical difference of the results of urine ${\beta}_2$-M excretion of several groups of renal diseases was analyzed with student T-test by using test results as dependent variables. Results : Levels of urine ${\beta}_2$-M excretion of the 102 patients were as follows : 52 had primary nephrotic syndrome [MCNS (n=45, $72{\pm}45{\mu}g/g$ creatinine, ${\mu}g/g-Cr$), MPGN (n=3, $154{\pm}415{\mu}g/g-Cr$), FSGS (n=4, $188{\pm}46{\mu}g/-Cr$], six had APSGN ($93{\pm}404{\mu}g/g-Cr$), seven had IgA nephropathy ($3,414{\pm}106{\mu}g/g-Cr$), 9 had APN ($742{\pm}160{\mu}g/g-Cr$), 16 had cystitis ($179{\pm}168{\mu}g/g-Cr$), and 12 had HSP nephritis ($109{\pm}898{\mu}g/g-Cr$). IgA nephropathy (P<0.05) and APN (P<0.05) were significantly higher than in other renal diseases. Among primary nephrotic syndrome, FSGS with higher results of ${\beta}_2$-microglobulin test had longer treatment period (P<0.01) when compared to the lower groups, but no significant differences in Ccr, BUN, or Cr were observed. Conclusion : IgA nephropathy and APN groups showed significantly higher level of ${\beta}_2$-M excretion value than other groups. Although ${\beta}_2$-microglobulin value is not appropriate as an indicator of general renal function and pathology, it seems to be sufficient in the differential diagnosis of the UTI and in the prediction of the treat-ment period of nephrotic syndrome patients.

• The Journal of the Korea Contents Association
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• v.20 no.7
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• pp.674-682
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• 2020

Recently, the problem of air pollution has become a global concern due to industrialization and overcrowding. Air pollution can cause various adverse effects on human health, among which respiratory diseases such as asthma, which have been of interest in this study, can be directly affected. Previous studies have used clinical data to identify how air pollutant affect diseases such as asthma based on relatively small samples. This is high likely to result in inconsistent results for each collection samples, and has significant limitations in that research is difficult for anyone other than the medical profession. In this study, the main focus was on predicting the actual asthmatic occurrence, based on data on the atmospheric environment data released by the government and the frequency of asthma outbreaks. First of all, this study verified the significant effects of each air pollutant with a time lag on the outbreak of asthma through the time-lag Pearson Correlation Coefficient. Second, train data built on the basis of verification results are utilized in Deep Learning algorithms, and models optimized for predicting the asthmatic occurrence are designed. The average error rate of the model was about 11.86%, indicating superior performance compared to other machine learning-based algorithms. The proposed model can be used for efficiency in the national insurance system and health budget management, and can also provide efficiency in the deployment and supply of medical personnel in hospitals. And it can also contribute to the promotion of national health through early warning of the risk of outbreak by atmospheric environment for chronic asthma patients.

• Proceedings of the KAIS Fall Conference
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• 2009.05a
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• pp.114-118
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• 2009

주요 일간지와 KBS 1 9시 뉴스에서 다루고 있는 건강관련 기사의 내용을 분석하여, 건강관련 정보의 경향을 파악하여, 건강 예보제 실시를 위한 현황 분석을 위해 본 연구를 실시하였다. 1. 일간지 건강정보 내용분석 결과요약: 기사 보도 분야에서는 생활건강(47.8%), 사회(14.6%), 종합(7.7%), 국제(4.7%), 경제(1.5%) 순이었고, 3/4분기에서만 국제와 경제 분야가 종합분야 보다 많이 보도 되었으며, 기타 분야가 22.9%로 많은 비율을 차지하였다. 건강정보를 대상 성별별로 분류 하였을 시 남녀 모두에 해당되는 자료가 대다수였고(87.9%), 여성이 대상인 정보(8.0%)가 남성(4.1%)보다 많았으며, 생애 주기별 대상으로 분류 하였을 시에 역시 특별히 구분되지 않은 모든 연령층에 해당 정보가 가장 많았고(60.8%), 성인(19.8%), 학동기 어린이(7.3%), 청소년 (4.9%), 노인(4.9%), 영유아(3.3%) 순이었다. 일간지별 기사의 주제를 조사한 결과 두 일간지의 총합은 병의 원인 및 위험인자(15.5%)가 가장 많이 보도 되었는데, 조선일보는 건강증진, 치료 및 술기, 투약, 기타의 순이었고, 한겨레신문은 건강증진, 행정, 치료 및 술기, 투약 순이었으며 각 주제별로 분기별로 약간의 차이를 나타내었다. 예측성에 관한 내용분석을 보면 예보성은 있는 경우(76.2%)가 없는 경우보다 많았고, 예보성의 정보는 알림성, 예측성, 행사성의 순이었고 예측성의 경우 건강형태가 가장 많았으나 한겨레 신문의 경우 기타에 속하는 경우가 가장 많았다. 사 ICD-10 체계, 21대 분류로 질병에 관한 기사를 분류한 결과 신생물(14.5%), 특정 감염성 및 기생충성 질환(13.6%), 정신 및 행동장애(9.5%)의 순이었으며 두 일간지간의 차이를 보였다. 2. TV 뉴스 건강정보 내용분석 결과요약: 건강정보의 대상 특징 성별은 모두 해당되는 경우가 265회 중 238회 (89.8%)로 가장 많았고, 생애주기별 대상으로 보면 모든 연령층에 해당 되는 것이 154회(58.1%)로 가장 많았다. 건강정보의 주제에 대해서 조사한 결과 병의 원인 및 위험인자가 73회(27.5%), 역학(역학조사 및 보도성)이 64회 (24.2%), 행정이 30회(11.3%), 증상 및 호소가 27회(10.2%) 등의 빈도순으로 조사 되었다. 건강정보의 내용의 분야를 보면 대분류로는 질병관리 분야가 102회(38.5%), 보건의료제도 및 행정 분야가 52회(19.6%), 보건행태 및 기타 42회(15.8%), 생활환경분야 39회(14.7%)의 빈도순으로 나타났다. 건강정보 내용을 질병의 분류 ICD-10 체계로 분석한 결과는 특정감염성 및 기생충성 질환 48회(26.8%), 소화기계의 질환, 손산, 중독 및 외인에 의한 특정기타 결과, 건강상태 및 보건서비스 접촉에 주는 요인 등의 빈도순으로 조사되었다.

• The Journal of Korean Orthopaedic Ultrasound Society
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• v.6 no.2
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• pp.53-59
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• 2013

Purpose: The aim of this study wasto evaluate the comparison of CT arthrography and ultrasonography, confirmed by the arthroscopic finding in patients with rotator cuff disease. Materials and Methods: We evaluated fifty seven patients with rotator cuff disease underwent CTA and arthroscopy, and twenty eight patients had taken ultrasonographyadditionally. The diagnostic value and prediction for tear size between CTA and ultrasonography were evaluated, as compared to arthroscopic findings. Results: CTA showed a sensitivity of 86.2% and a specificity of 100% in full thickness tear ofsupraspinatus, a sensitivity of 58.3% and a specificity of 87.8% in partial-thickness tear. CTA demonstrated good diagnostic value for full thickness tear, but there was relatively lower value for partial-thickness tear. Ultrasonography showed a sensitivity of 84.6% and a specificity of 86.7% for diagnosing in full thickness tear, a sensitivity of 84.6% and a specificity of 73.3% in partial-thickness tear. Ultrasonography provided good diagnostic value, but, there is lesser accurate result for prediction of tear size. Conclusion: CTA showedgood diagnostic tool of detection full-thickness tear of rotator cuff disease and predicting of tear size. Comparing with ultrasonography, CTA was inferior for detection of partial-thickness tear, but, provided better estimation for tear size.

• The Journal of the Korea Contents Association
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• v.21 no.8
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• pp.662-674
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• 2021

Metabolic syndrome is closely associated with cardiovascular disease, there is increasing attentions in prevention of metabolic syndrome through prediction. The aim of this study was to systematically review the literature by collecting, analyzing, and synthesizing articles of predicting metabolic syndrome in Koreans. For systemic review, data search was conducted on Global journals Pubmed, WoS and domestic journals DBPia, KISS published in 2011-2020 year. Three keyword 'Metabolic syndrome', 'predict', and 'korea' were used for searching under AND condition. Total 560 articles were searched and the final 22 articles were selected according to the data selection criteria. The most useful variable was WHtR(AUC=0.897), most frequently used analysis method was logistic regression(63.6%), and most accurate analysis method was XGBOOST(AUC=0.879) for predicting metabolic syndrome. Prediction accuracy was slightly improved when sasang constitution types was used. Based on the results of this study, it is believed that various large-scale longitudinal studies for the prediction and management of the Metabolic syndrome in Korean should be followed in the future.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.5
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• pp.999-1008
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• 2023

Early prediction of chronic diseases such as diabetes is an important issue, and improving the accuracy of diabetes prediction is especially important. Various machine learning and deep learning-based methodologies are being introduced for diabetes prediction, but these technologies require large amounts of data for better performance than other methodologies, and the learning cost is high due to complex data models. In this study, we aim to verify the claim that DNN using the pima dataset and k-fold cross-validation reduces the efficiency of diabetes diagnosis models. Machine learning classification methods such as decision trees, SVM, random forests, logistic regression, KNN, and various ensemble techniques were used to determine which algorithm produces the best prediction results. After training and testing all classification models, the proposed system provided the best results on XGBoost classifier with ADASYN method, with accuracy of 81%, F1 coefficient of 0.81, and AUC of 0.84. Additionally, a domain adaptation method was implemented to demonstrate the versatility of the proposed system. An explainable AI approach using the LIME and SHAP frameworks was implemented to understand how the model predicts the final outcome.

• Tuberculosis and Respiratory Diseases
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• v.54 no.4
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• pp.395-402
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• 2003

Background : Patients with chronic obstructive pulmonary disease (COPD) are at increased risk for osteoporosis, which has implications for mobility and even mortality. The goal of this pilot study was to evaluate bone mineral density (BMD) and risk factors for osteoporosis in a limited number of men with COPD. Methods : We checked BMD, $FEV_1$(% of predicted) and investigated risk factors for osteoporosis in 44 male patients with COPD who visited our hospital from January to August 2002. Results : Mean(${\pm}$) age was $69{\pm}9$ yrs, body mass index(BMI) $21{\pm}3kg/m^2$, $FEV_1$ $50{\pm}18%$ of predicted, lumbar spine T-score $-3.0{\pm}1.2$, lumbar spine Z-score $-2.0{\pm}1.2$, and lumbar spine BMD $0.76{\pm}0.13g/cm^2$. Osteoporosis(T-score below -2.5) was present in 27 patients(61.4%) and osteopenia(T-score between -1 and -2.5) in 17(38.6%). None of the patients had normal BMD. There was no relationship between BMD and $FEV_1$(% of predicted). There were significant differences in smoking, alcohol consumption, exercise, cumulative steroid dose, BMI and BMD among the three groups according to $FEV_1$(% of predicted) (group1 : ${\geq}65%$, group2 : 50-64%, group3 : ${\leq}49%$), except age. However, there were no significant differences in these variables between the osteopenia and osteoporosis groups, except BMI. Linear Regression(Stepwise) analysis showed that lumbar BMD was correlated with BMI & exercise. Conclusion : BMD is significantly reduced in men with COPD. There was no relationship between BMD and pulmonary function.

• The Journal of the Korea Contents Association
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• v.10 no.10
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• pp.54-67
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• 2010

Applying the conventional machine-learning method which has been frequently used in health-care area has several fundamental problems for modern U-health service analysis. First of all, we are still lack of application examples of the traditional method for our modern U-health environment because of its short term history of U-health study. Second, it is difficult to apply the machine-learning method to our U-health service environment which requires real-time management of disease because the method spends a lot of time in the process of learning. Third, we cannot implement a personalized U-health diagnosis system using the conventional method because there is no way to assign weights on the disease-related variables although various kinds of machine-learning schemes have been proposed. In this paper, a novel diagnosis scheme PCADP is proposed to overcome the problems mentioned above. PCADP scheme is a personalized diagnosis method and it makes the bio-data analysis just a 'process' in the U-health service system. In addition, we offer a semantics modeling of the U-health ontology framework in order to describe U-health data and service specifications as meaningful representations based on this PCADP. The PCADP scheme is a kind of statistical diagnosis method which has characteristics of flexible structure, real-time processing, continuous improvement, and easy monitoring of decision process. Upto the best of authors' knowledge, the PCADP scheme and ontology framework proposed in this paper reveals one of the best characteristics of flexible structure, real-time processing, continuous improvement, and easy monitoring among recently developed U-health schemes.

• Journal of Environmental Impact Assessment
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• v.33 no.1
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• pp.9-17
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• 2024

The purpose of this study is to select and predict optimal heatwave indices for describing and predicting heat-related illnesses. Regression analysis was conducted using Heat-related illness surveillance system data for a number of heat-related illnesses and meteorological data from the Korea Meteorological Administration's Automatic Weather Station (AWS) for the period from 2021 to 2023. Daily average temperature, daily maximum temperature, daily average Wet Bulb Globe Temperature (WBGT), and daily maximum WBGT values were calculated and analyzed. The results indicated that among the four indicators, the daily maximum WBGT showed the highest suitability with an R² value of 0.81 and RMSE of 0.98, with a threshold of 29.94 Celsius. During the entire analysis period, there were a total of 91 days exceeding this threshold, resulting in 339 cases of heat-related illnesses. Predictions of heat-related illness cases from 2021 to 2023 using the regression equation for daily maximum WBGT showed an accuracy with less than 10 cases of error annually, demonstrating a high level of precision. Through continuous research and refinement of data and analysis methods, it is anticipated that this approach could contribute to predicting and mitigating the impact of heatwaves.

• Journal of Life Science
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• v.20 no.12
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• pp.1758-1763
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• 2010

Osteoporosis is a complex systemic skeletal disease and a major public health concern worldwide. It is a heritable disorder characterized mainly by low bone density and/or low trauma osteoporotic fractures, both of which have strong genetic determination. However, the specific genetic variants determining risk for low bone density are still largely unknown. Here, we performed association analysis to elucidate the possible relationship between genetic polymorphisms in the SQSTM1 gene and low bone density. By examining a total of 7225 (men: 3622, women: 3603) subjects from the Korean population in the Korean Association REsource (KARE) study, we discovered that SQSTM1 gene polymorphisms were associated with bone density. The results of the BD-RT (bone density estimated by T-score at distal radius) showed that three SNPs (rs513235, rs3734007, and rs11249661) within the SQSTM1 gene were significantly associated with bone density. The results of the BD-TT (bone density estimated by T-score at midshaft tibia) showed that four SNPs (rs513235, rs3734007, rs2241349, and rs11249661) were significantly associated with bone density. The three SNPs (rs513235, rs3734007, and rs11249661) had common significance in both BD-RT and BD-TT. In summary, we found statistically significant SNPs in the SQSTM1 gene that are associated with bone density traits. Therefore, our findings suggest SQSTM1 gene could be related to pathogenesis of osteoporosis.