• Asian Pacific Journal of Cancer Prevention
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• 제15권6호
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• pp.2785-2791
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• 2014

Aims: To analyze time-dependent changes in female breast cancer (BC) mortality in China, forecast the trend in the ensuing 5 years, and provide recommendations for prevention and management. Materials and Methods: Mortality data of breast cancer in China from 1991 to 2011 was used to describe characteristics and distribution, such as the changes of the standardized mortality rate, urban-rural differences and age differences. Trend-surface analysis was used to study the geographical distribution of mortality. In addition, curve estimation, time series modeling, Gray modeling (GM) and joinpoint regression were performed to estimate and predict future trends. Results: In China, the mortality rate of breast cancer has increased yearly since 1991. In addition, our data predicted that the trend will continue to increase in the ensuing 5 years. Rates in urban areas are higher than those in rural areas. Over the past decade, all peak ages for death by breast cancer have been delayed, with the first death peak occurring at 55 to 65 years of age in urban and rural areas. Geographical analysis indicated that mortality rates increased from Southwest to Northeast and from West to East. Conclusions: The standardized mortality rate of breast cancer in China is rising and the upward trend is predicted to continue for the next 5 years. Since this can cause an enormous health impact in China, much better prevention and management of breast cancer is needed. Consequently, disease control centers in China should place more focus on the northeastern, eastern and southeastern parts of China for breast cancer prevention and management, and the key population should be among women between ages 55 to 65, especially those in urban communities.

• Asian Pacific Journal of Cancer Prevention
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• 제16권15호
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• pp.6729-6734
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• 2015

Background: To analyze the mortality distribution of nasopharyngeal carcinoma in China from 1991 to 2013, to predict the mortality in the ensuing five years, and to provide evidence for prevention and treatment of nasopharyngeal carcinoma. Materials and Methods: Mortality data for Nasopharyngeal Carcinoma in China from 1991 to 2013 were used to describe its epidemiological characteristics, such as the change of the standardized mortality rate, sex and age differences, urban-rural differences. Trend-surface analysis was used to study the geographical distribution of the mortality. Curve estimation, time series, gray modeling, and joinpoint regression were used to predict the mortality for the ensuing five years in the future. Results: In China, the standardized mortality rate of Nasopharyngeal Carcinoma increased with time from 1996, reaching the peak values of $1.45/10^5$ at the year of 2002, and decreased gradually afterwards. With males being 1.51 times higher than females, and the city had a higher rate than the rural during the past two decades. The mortality rate increased from age 40. Geographical analysis showed the mortality rate increased from middle to southern China. Conclusions: The standardized mortality rate of Nasopharyngeal Carcinoma is falling. The regional disease control for Nasopharyngeal Carcinoma should be focused on Guangdong province of China, and the key targets for prevention and treatment are rural men, especially after the age of 40. The mortality of Nasopharyngeal Carcinoma will decrease in the next five years.

• 한국해안해양공학회지
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• 제19권2호
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• pp.128-135
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• 2007

해양수산부에서 연안에 설치한 기상 관측 장비로 측정된 우리나라 세 지역(낙동강 하구, 시화호, 마산만)의 기온과 수온 자료를 바탕으로 기온-수온의 비선형 상관관계를 분석하였다. 기온-수온 대표관계인 비선형 S-모형의 관계식에 포함된 4개의 매개변수를 결정하기 위해서 SCE 최적화 기법을 이용하였으며, 마산만 지역에서는 계절적 이력현상을 고려하여 서로 다른 관계식을 적용하였다. 기온-수온 상관 관계는 시간규모에 따른 최대 온도값과 최소 온도값에 차이가 있으나 수질 또는 생태 반응의 적당한 시간규모에 해당하는 주 평균 온도값을 이용하여 분석하였다. 전반적으로 연구지역인 하천과 해안이 접하는 우리나라 해안 및 하구 지역에서는 S-모형의 관계식이 선형 관계식보다 적합한 것으로 파악되었다. 이 연구는 기후변화 가설에 따른 미래의 수온변화에 반응하는 수질, 수문, 및 생태반응을 모의하여 공학기술자 또는 정책입안자에게 적절한 기후변화 대책 방향을 설정하는데 기여할 것으로 사료된다.

• 한국대기환경학회지
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• 제24권1호
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• pp.43-54
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• 2008

Management and control of ammonia at the sources and ambient largely depend on sampling and measurement techniques. Good sampling and measurement techniques provide high quality data. The main purpose of the study is compare the analytical characteristics of the Indolphenol method which is one of the standard method in Korea with automatic analyzers for continued measuring gaseous ammonia. For comparison with other analytical methods, the verification test was designed to evaluate performance parameters; linearity, absorption efficiency, reproducibility and repeatability test, accuracy, and response time test. $R^2$ of calibration curve using IPM and CLM was very high (value is 1.000), but for EcSM $R^2$ value was estimated to be lower than IPM and CLM (as 0.991). The RSD of the CLM ranged from 0.1 to 2.3% over the nine concentration levels measured, %Ds was 0.1 to 10.7%, and average RA over all the measurements was 3.3%. The RSD of IPM and EcSM was ranged from 1.0 to 8.1, 3.9 to 14.0 respectively, and average RA were 8.71, 4.9% respectively. Rise in response times of EcSM was estimated to be 1 minute. It is found to be more sensitive than response time (which ranged from 2 to 9 minute) of CLM. For ammonia concentration measured using the IPM and the CLM from the same ammonia source, linear regression of IPM versus CLM show a slope of 0.805, an intercept of 637 ppb, and $R^2$ of 0.868.

• Tuberculosis and Respiratory Diseases
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• 제85권4호
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• pp.349-357
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• 2022

Background: The most consistently identified mortality determinants for the new coronavirus 2019 (COVID-19) infection are aging, male sex, cardiovascular/respiratory diseases, and cancer. They were determined from heterogeneous cohorts that included patients with different disease severity and previous conditions. The main goal of this study was to determine if activities of daily living (ADL) dependence measured by Barthel's index could be a predictor for COVID-19 mortality. Methods: A prospective cohort study was performed with a consecutive sample of 340 COVID-19 patients representing patients from all over the northern region of Portugal from October 2020 to March 2021. Mortality risk factors were determined after controlling for demographics, ADL dependence, admission time, comorbidities, clinical manifestations, and delay-time for diagnosis. Central tendency measures were used to analyze continuous variables and absolute numbers (proportions) for categorical variables. For univariable analysis, we used t test, chi-square test, or Fisher exact test as appropriate (α=0.05). Multivariable analysis was performed using logistic regression. IBM SPSS version 27 statistical software was used for data analysis. Results: The cohort included 340 patients (55.3% females) with a mean age of 80.6±11.0 years. The mortality rate was 19.7%. Univariate analysis revealed that aging, ADL dependence, pneumonia, and dementia were associated with mortality and that dyslipidemia and obesity were associated with survival. In multivariable analysis, dyslipidemia (odds ratio [OR], 0.35; 95% confidence interval [CI], 0.17-0.71) was independently associated with survival. Age ≥86 years (pooled OR, 2.239; 95% CI, 1.100-4.559), pneumonia (pooled OR, 3.00; 95% CI, 1.362-6.606), and ADL dependence (pooled OR, 6.296; 95% CI, 1.795-22.088) were significantly related to mortality (receiver operating characteristic area under the curve, 82.1%; p<0.001). Conclusion: ADL dependence, aging, and pneumonia are three main predictors for COVID-19 mortality in an elderly population.

• Journal of Korean Neurosurgical Society
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• 제67권1호
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• pp.94-102
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• 2024

Objective : The spontaneous intracerebral hemorrhage (ICH) remains a significant cause of mortality and morbidity throughout the world. The purpose of this retrospective study is to develop multiple models for predicting ICH outcomes using machine learning (ML). Methods : Between January 2014 and October 2021, we included ICH patients identified by computed tomography or magnetic resonance imaging and treated with surgery. At the 6-month check-up, outcomes were assessed using the modified Rankin Scale. In this study, four ML models, including Support Vector Machine (SVM), Decision Tree C5.0, Artificial Neural Network, Logistic Regression were used to build ICH prediction models. In order to evaluate the reliability and the ML models, we calculated the area under the receiver operating characteristic curve (AUC), specificity, sensitivity, accuracy, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR). Results : We identified 71 patients who had favorable outcomes and 156 who had unfavorable outcomes. The results showed that the SVM model achieved the best comprehensive prediction efficiency. For the SVM model, the AUC, accuracy, specificity, sensitivity, PLR, NLR, and DOR were 0.91, 0.92, 0.92, 0.93, 11.63, 0.076, and 153.03, respectively. For the SVM model, we found the importance value of time to operating room (TOR) was higher significantly than other variables. Conclusion : The analysis of clinical reliability showed that the SVM model achieved the best comprehensive prediction efficiency and the importance value of TOR was higher significantly than other variables.

• 한국수자원학회논문집
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• 제40권8호
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• pp.655-663
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• 2007

본 연구는 면적이 작은 도시유역에 적용하기 위한 강우의 시간분포방법을 제안하였다. 이를 위하여 유역의 특성을 고려한 Interevent Time Definition(ETD) 결정방법과 3시간 미만의 강우를 포함한 무차원 누가우량곡선을 제안하였다. 새롭게 제시된 IETD 결정방법은 호우 사상의 종료 시점으로부터 직접유출의 종료 시점까지의 기간을 IETD로 정의하였다. 제안된 IETD 결정방법을 사용하여 중랑 배수구역에 대한 면적-IETD 관계곡선을 유도하였다. 제안된 강우분포모형을 4개의 도시 유역에 적용하여 10년빈도 2시간 강우를 시간분포 시켰으며, SWMM을 사용하여 유출수문곡선을 산정하였다. 산정된 유출수문곡선을 Huff 방법으로 산정한 유출수문곡선과 비교하였다. 비교한 결과 새롭게 제시된 강우의 시간분포 방법에 의한 유역의 첨두 홍수량은 Huff 모형에 의한 결과들보다 $11\sim15%$크게 나타났으며, 따라서 기존의 Huff 모형을 도시유역에 그대로 적용하는 것은 유출을 과소평가 할 위험성을 내포하고 있다.

• 지능정보연구
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• 제27권3호
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• pp.57-73
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• 2021

ICT 인프라의 이상탐지를 통한 유지보수와 장애 예방이 중요해지고 있다. 장애 예방을 위해서 이상탐지에 대한 관심이 높아지고 있으며, 지금까지의 다양한 이상탐지 기법 중 최근 연구들에서는 딥러닝을 활용하고 있으며 오토인코더를 활용한 모델을 제안하고 있다. 이는 오토인코더가 다차원 다변량에 대해서도 효과적으로 처리가 가능하다는 것이다. 한편 학습 시에는 많은 컴퓨터 자원이 소모되지만 추론과정에서는 연산을 빠르게 수행할 수 있어 실시간 스트리밍 서비스가 가능하다. 본 연구에서는 기존 연구들과 달리 오토인코더에 2가지 요소를 가미하여 이상탐지의 성능을 높이고자 하였다. 먼저 다차원 데이터가 가지고 있는 속성별 특징을 최대한 부각하여 활용하기 위해 멀티모달 개념을 적용한 멀티모달 오토인코더를 적용하였다. CPU, Memory, network 등 서로 연관이 있는 지표들을 묶어 5개의 모달로 구성하여 학습 성능을 높이고자 하였다. 또한, 시계열 데이터의 특징을 데이터의 차원을 늘리지 않고 효과적으로 학습하기 위하여 조건부 오토인코더(conditional autoencoder) 구조를 활용하는 조건부 멀티모달 오토인코더(Conditional Multimodal Autoencoder, CMAE)를 제안하였다. 제안한 CAME 모델은 비교 실험을 통해 검증했으며, 기존 연구들에서 많이 활용된 오토인코더와 비교하여 AUC, Accuracy, Precision, Recall, F1-score의 성능 평가를 진행한 결과 유니모달 오토인코더(UAE)와 멀티모달 오토인코더(Multimodal Autoencoder, MAE)의 성능을 상회하는 결과를 얻어 이상탐지에 있어 효과적이라는 것을 확인하였다.

• Asian Pacific Journal of Cancer Prevention
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• 제16권12호
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• pp.5095-5099
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• 2015

Background: The statistical methods to analyze and predict the related dangerous factors of deep fungal infection in lung cancer patients were several, such as logic regression analysis, meta-analysis, multivariate Cox proportional hazards model analysis, retrospective analysis, and so on, but the results are inconsistent. Materials and Methods: A total of 696 patients with lung cancer were enrolled. The factors were compared employing Student's t-test or the Mann-Whitney test or the Chi-square test and variables that were significantly related to the presence of deep fungal infection selected as candidates for input into the final artificial neural network analysis (ANN) model. The receiver operating characteristic (ROC) and area under curve (AUC) were used to evaluate the performance of the artificial neural network (ANN) model and logistic regression (LR) model. Results: The prevalence of deep fungal infection from lung cancer in this entire study population was 32.04%(223/696), deep fungal infections occur in sputum specimens 44.05%(200/454). The ratio of candida albicans was 86.99% (194/223) in the total fungi. It was demonstrated that older (${\geq}65$ years), use of antibiotics, low serum albumin concentrations (${\leq}37.18g/L$), radiotherapy, surgery, low hemoglobin hyperlipidemia (${\leq}93.67g/L$), long time of hospitalization (${\geq}14$days) were apt to deep fungal infection and the ANN model consisted of the seven factors. The AUC of ANN model($0.829{\pm}0.019$)was higher than that of LR model ($0.756{\pm}0.021$). Conclusions: The artificial neural network model with variables consisting of age, use of antibiotics, serum albumin concentrations, received radiotherapy, received surgery, hemoglobin, time of hospitalization should be useful for predicting the deep fungal infection in lung cancer.

• Journal of Gastric Cancer
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• 제8권1호
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• pp.27-34
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• 2008

목적: 복강경 보조 위전절제술은 기술적 어려움과 환자수가 많지 않음으로 인하여 복강경 보조 위원위부 절제술에 비하여 연구가 많지 않은 상황이다. 따라서 저자들은 본원에서 시행한 복강경 보조 위전절제술의 수술성적과 수술후 경과 및 합병증 발생 예측인자와 복강경 보조 위전절제술의 학습곡선에 관하여 분석하였다. 대상 및 방법: 2005년 1월부터 2007년 9월까지 술 전 검사상 위의 중 상부에 위치한 조기위암(cT1N0)을 진단받고 한 술자에 의해 복강경 보조 위전절제술을 시행 받은 연속적인 48명의 환자를 대상으로 의무기록을 바탕을 후향적으로 분석하였다. 결과: 수술 중 합병증이나 개복수술로의 전환은 없었고 평균 수술시간은 $212{\pm}67$분이었다. 평균 적출 림프절 개수는 $29{\pm}10$개였고 모든 환자에서 안전한 종양 경계 면이 확보되었다. 수술 후 가스배출, 식이시작, 퇴원시기는 각각 평균 2.98일, 3.67일, 7.08일이었다. 외과적 합병증은 5명(10.4%)에서 발생하였고 모두 보전적 치료로 호전되었다. 단변량 및 다변량 분석에서 합병증 발생에 영향을 주는 유일한 인자는 체질량지순(P=0.035, HR=2.462)였으며 수술 시간을 기준으로 한 학습곡선 분석에서 20예가 학습곡선인 것으로 나타났다. 결론: 복강경 보조 위전절제술은 위의 중 상부에 위치한 조기위암에 대한 적절한 술식이며 수술성적과 수술 후 경과가 양호하다. 그러나 학습곡선을 단축하기 위해서는 복강경 보조 위원위부절제술의 충분한 경험이 필요하며, 특히 초기경험에서는 수술 후 합병증을 줄이기 위하여 환자 선택에 있어서 체질량지수를 고려하는 게 도움이 될 것으로 생각된다.