원貨切上에 따른 우리나라企業의 對應戰略

柳昌源 건국대학교 사회과학대학 무역학과 1988 貿易硏究 Vol.6 No.-

Big Data Analysis Using Modern Statistical and Machine Learning Methods in Medicine

유창원,Luis Ramirez,Juan Liuzzi 대한배뇨장애요실금학회 2014 International Neurourology Journal Vol.18 No.2

In this article we introduce modern statistical machine learning and bioinformatics approaches that have been used in learning statistical relationships from big data in medicine and behavioral science that typically include clinical, genomic (and proteomic) and environmental variables. Every year, data collected from biomedical and behavioral science is getting larger and more complicated. Thus, in medicine, we also need to be aware of this trend and understand the statistical tools that are available to analyze these datasets. Many statistical analyses that are aimed to analyze such big datasets have been introduced recently. However, given many different types of clinical, genomic, and environmental data, it is rather uncommon to see statistical methods that combine knowledge resulting from those different data types. To this extent, we will introduce big data in terms of clinical data, single nucleotide polymorphism and gene expression studies and their interactions with environment. In this article, we will introduce the concept of well-known regression analyses such as linear and logistic regressions that has been widely used in clinical data analyses and modern statistical models such as Bayesian networks that has been introduced to analyze more complicated data. Also we will discuss how to represent the interaction among clinical, genomic, and environmental data in using modern statistical models. We conclude this article with a promising modern statistical method called Bayesian networks that is suitable in analyzing big data sets that consists with different type of large data from clinical, genomic, and environmental data. Such statistical model form big data will provide us with more comprehensive understanding of human physiology and disease.

워게임 連動保安을 위한 政策基盤의 객체 保安화 방법과 管理시스템 제안

유창원 ( Chang-won Yu ),이희조 ( Heel-jo Lee ) 한국정보처리학회 2010 한국정보처리학회 학술대회논문집 Vol.17 No.1

본 논문은 국방 네트워크 환경측면에서 실 전략정보에 대한 접근/활용 범위가 확대되고, 정보의 신뢰성/가용성에 대한 중요도가 강조되고 있는 워게임 연동객체의 보안화 방안에 대해 기술한다. 연동객체는 HLA/RTI에서 시뮬레이션을 위한 페더레이터로 정의된다. RTI는 페더레이터간 정보교환, 접속 사양에 정의된 다양한 서비스를 제공하는 미들웨어이다. 정책을 기반으로 네트워크상에서 자원들에 대한 관리와 접근, 사용을 위한 다양한 기능과 안전하고 편리한 보안기능들이 널리 활용되고 있다. 본 논문에서는 RTI Security 인터페이스와 정책을 기반으로 한 자원접근 방법을 사용해서 페더레이터의 취약한 정보보호 기능을 강화하였다. RTI의 페더레이트 실 정보에 다계층 보안 수준을 적용하고 보안등급별로 필터링하여 연동객체 스스로가 정보를 보호(Self-protection)할 수 있도록 하는 환경을 구성하고 관리할 수 있는 시스템을 제안한다.

Development of Decision Support Formulas for the Prediction of Bladder Outlet Obstruction and Prostatic Surgery in Patients With Lower Urinary Tract Symptom/Benign Prostatic Hyperplasia: Part I, Development of the Formula and its Internal Validation

추민수,유창원,조성용,정성진,정창욱,구자현,오승준 대한배뇨장애요실금학회 2017 International Neurourology Journal Vol.21 No.S1

Purpose: As the elderly population increases, a growing number of patients have lower urinary tract symptom (LUTS)/benign prostatic hyperplasia (BPH). The aim of this study was to develop decision support formulas and nomograms for the prediction of bladder outlet obstruction (BOO) and for BOO-related surgical decision-making, and to validate them in patients with LUTS/BPH. Methods: Patient with LUTS/BPH between October 2004 and May 2014 were enrolled as a development cohort. The available variables included age, International Prostate Symptom Score, free uroflowmetry, postvoid residual volume, total prostate volume, and the results of a pressure-flow study. A causal Bayesian network analysis was used to identify relevant parameters. Using multivariate logistic regression analysis, formulas were developed to calculate the probabilities of having BOO and requiring prostatic surgery. Patients between June 2014 and December 2015 were prospectively enrolled for internal validation. Receiver operating characteristic curve analysis, calibration plots, and decision curve analysis were performed. Results: A total of 1,179 male patients with LUTS/BPH, with a mean age of 66.1 years, were included as a development cohort. Another 253 patients were enrolled as an internal validation cohort. Using multivariate logistic regression analysis, 2 and 4 formulas were established to estimate the probabilities of having BOO and requiring prostatic surgery, respectively. Our analysis of the predictive accuracy of the model revealed area under the curve values of 0.82 for BOO and 0.87 for prostatic surgery. The sensitivity and specificity were 53.6% and 87.0% for BOO, and 91.6% and 50.0% for prostatic surgery, respectively. The calibration plot indicated that these prediction models showed a good correspondence. In addition, the decision curve analysis showed a high net benefit across the entire spectrum of probability thresholds. Conclusions: We established nomograms for the prediction of BOO and BOO-related prostatic surgery in patients with LUTS/BPH. Internal validation of the nomograms demonstrated that they predicted both having BOO and requiring prostatic surgery very well.

베이시안 기계학습과 의학 빅데이터를 이용한 인과관계 유전자 조절 네트워크의 그래픽 모델 개발

박성배,유창원 대한의사협회 2022 대한의사협회지 Vol.65 No.3

Background: Data collection from medicine and biomedical science is becoming a large task and increasingly complicated with each passing day. Machine learning methods have been applied to elucidate interactions between genes and genes and their environment. Current Concepts: Many machine learning methods have been used to determine the statistical meaning or relationship in the prediction or progression of diseases through the creation of causal networks based on medical big data. Through these analyses, the occurrence and progression of diseases have been shown to be related to several genes and environmental factors. However, these methods cannot identify the key upstream regulators inferred from genomic, clinical, and environmental medical data. Discussion and Conclusion: The causal Bayesian network (CBN) is a machine learning method that can be used to understand a causal network inferred from the gene expression data. The CBN can help identify the key upstream regulators through examining the causal network inferred from medical big data having genomic information. We can easily improve the clinical outcome through regulation of these identified key upstream factors. Therefore, the CBN may be a powerful and flexible tool in the era of precision medicine.

Development of Decision Support Formulas for the Prediction of Bladder Outlet Obstruction and Prostatic Surgery in Patients With Lower Urinary Tract Symptom/Benign Prostatic Hyperplasia: Part II, External Validation and Usability Testing of a Smartphone

추민수,정성진,조성용,유창원,정창욱,구자현,오승준 대한배뇨장애요실금학회 2017 International Neurourology Journal Vol.21 No.S1

Purpose: We aimed to externally validate the prediction model we developed for having bladder outlet obstruction (BOO) and requiring prostatic surgery using 2 independent data sets from tertiary referral centers, and also aimed to validate a mobile app for using this model through usability testing. Methods: Formulas and nomograms predicting whether a subject has BOO and needs prostatic surgery were validated with an external validation cohort from Seoul National University Bundang Hospital and Seoul Metropolitan Government-Seoul National University Boramae Medical Center between January 2004 and April 2015. A smartphone-based app was developed, and 8 young urologists were enrolled for usability testing to identify any human factor issues of the app. Results: A total of 642 patients were included in the external validation cohort. No significant differences were found in the baseline characteristics of major parameters between the original (n=1,179) and the external validation cohort, except for the maximal flow rate. Predictions of requiring prostatic surgery in the validation cohort showed a sensitivity of 80.6%, a specificity of 73.2%, a positive predictive value of 49.7%, and a negative predictive value of 92.0%, and area under receiver operating curve of 0.84. The calibration plot indicated that the predictions have good correspondence. The decision curve showed also a high net benefit. Similar evaluation results using the external validation cohort were seen in the predictions of having BOO. Overall results of the usability test demonstrated that the app was user-friendly with no major human factor issues. Conclusions: External validation of these newly developed a prediction model demonstrated a moderate level of discrimination, adequate calibration, and high net benefit gains for predicting both having BOO and requiring prostatic surgery. Also a smartphone app implementing the prediction model was user-friendly with no major human factor issue.

Factors Influencing Nonabsolute Indications for Surgery in Patients With Lower Urinary Tract Symptoms Suggestive of Benign Prostatic Hyperplasia: Analysis Using Causal Bayesian Networks

김명,Luis Ramirez,유창원,추민수,백재승,오승준 대한배뇨장애요실금학회 2014 International Neurourology Journal Vol.18 No.4

Purpose: To identify the factors affecting the surgical decisions of experienced physicians when treating patients with lowerurinary tract symptoms that are suggestive of benign prostatic hyperplasia (LUTS/BPH). Methods: Patients with LUTS/BPH treated by two physicians between October 2004 and August 2013 were included in thisstudy. The causal Bayesian network (CBN) model was used to analyze factors influencing the surgical decisions of physiciansand the actual performance of surgery. The accuracies of the established CBN models were verified using linear regression (LR)analysis. Results: A total of 1,108 patients with LUTS/BPH were analyzed. The mean age and total prostate volume (TPV) were 66.2(±7.3, standard deviation) years and 47.3 (±25.4) mL, respectively. Of the total 1,108 patients, 603 (54.4%) were treated byphysician A and 505 (45.6%) were treated by physician B. Although surgery was recommended to 699 patients (63.1%), 589(53.2%) actually underwent surgery. Our CBN model showed that the TPV (R=0.432), treating physician (R=0.370), bladderoutlet obstruction (BOO) on urodynamic study (UDS) (R=0.324), and International Prostate Symptom Score (IPSS) question3 (intermittency; R=0.141) were the factors directly influencing the surgical decision. The transition zone volume(R=0.396), treating physician (R=0.340), and BOO (R=0.300) directly affected the performance of surgery. Compared to theLR model, the area under the receiver operating characteristic curve of the CBN surgical decision model was slightly compromised(0.803 vs. 0.847, P<0.001), whereas that of the actual performance of surgery model was similar (0.801 vs. 0.820,P=0.063) to the LR model. Conclusions: The TPV, treating physician, BOO on UDS, and the IPSS item of intermittency were factors that directly influenceddecision-making in physicians treating patients with LUTS/BPH.

Causal Inference Network of Genes Related with Bone Metastasis of Breast Cancer and Osteoblasts Using Causal Bayesian Networks

박성배,정춘기,Efrain Gonzalez,유창원 대한골대사학회 2018 대한골대사학회지 Vol.25 No.4

Background: The causal networks among genes that are commonly expressed in osteoblasts and during bone metastasis (BM) of breast cancer (BC) are not well understood. Here, we developed a machine learning method to obtain a plausible causal network of genes that are commonly expressed during BM and in osteoblasts in BC. Methods: We selected BC genes that are commonly expressed during BM and in osteoblasts from the Gene Expression Omnibus database. Bayesian Network Inference with Java Objects (Banjo) was used to obtain the Bayesian network. Genes registered as BC related genes were included as candidate genes in the implementation of Banjo. Next, we obtained the Bayesian structure and assessed the prediction rate for BM, conditional independence among nodes, and causality among nodes. Furthermore, we reported the maximum relative risks (RRs) of combined gene expression of the genes in the model. Results: We mechanistically identified 33 significantly related and plausibly involved genes in the development of BC BM. Further model evaluations showed that 16 genes were enough for a model to be statistically significant in terms of maximum likelihood of the causal Bayesian networks (CBNs) and for correct prediction of BM of BC. Maximum RRs of combined gene expression patterns showed that the expression levels of UBIAD1, HEBP1, BTNL8, TSPO, PSAT1, and ZFP36L2 significantly affected development of BM from BC. Conclusions: The CBN structure can be used as a reasonable inference network for accurately predicting BM in BC.

상이한 해상도를 갖는 창조21모델과 전투21모델의 연동간 간접전투 피해평가 일치 방안 연구

한석원,문호석,최연호,유창원 한국데이터정보과학회 2019 한국데이터정보과학회지 Vol.30 No.5

This study suggests a method to improve the reliability of training with war game models by matching the results of the artillery damage assessment of the training units generated in the same situation when linking ChangJo21 and Combat21 models which are training war game models with different resolution. If the results of the artillery damage assessment of the ChangJo21 model and the Combat21 model that occurred in the same situation are not similar at a reasonable level, it is likely that the training unit will distrust the training with the war game models. In this study, the artillery damage assessment logic and formulas of the two models were compared and analyzed to compensate for differences in artillery damage assessment results of the two models, thereby establishing an experimental environment that could occur under the same conditions and generating experimental data. Multiple regression models as calibration models were used to match the artillery damage results of the two models. Each calibration model is statistically significant and highly explanatory. 본 연구는 상이한 해상도의 훈련용 워게임 모델인 창조21모델과 전투21모델을 연동 시 동일한 상황에서 발생되는 훈련부대의 포병 피해평가 결과를 유사하게 일치시켜, 워게임 모델을 활용한 훈련의 신뢰도를 향상시키는 방안을 제시하는 연구이다. 만약 동일한 상황에서 발생한 창조21모델의 포병 피해평가 결과와 전투21모델의 포병 피해평가 결과가 합리적인 수준에서 유사하지 않으면, 훈련 부대가 워게임 모델을 활용한 훈련을 불신할 가능성이 높다. 본 연구에서는 두 모델의 포병 피해평가 결과 차이를 보정하기 위해서 두 모델의 포병 피해평가 논리와 수식을 비교 분석하였고, 이를 통해서 동일한 조건 하에서 발생될 수 있는 실험환경을 구축하고 실험데이터를 생성하였다. 두 모델의 포병 피해결과를 일치시키기 위해서 보정 모형으로 다중회귀모형이 사용되었다. 각각의 보정 모형은 통계적으로 유의하고 설명력이 높아서 두 모델의 포병 피해평가 결과를 유사하게 보정하는데 사용될 수 있다. 본 연구는 상이한 해상도를 가진 C-C 모델간의 연동 시에 데이터 해상도 일치와 관련해서 특히 포병 피해평가 결과를 일치시키는 방안을 최초로 다룬 연구로 임의의 C-C연동 시에도 활용될 수 있다.

FRAGSTATS 모형을 이용한 도암댐 유역의 경관 분석

허성구 ( Sung-gu Heo ),김기성 ( Ki-sung Kim ),안재훈 ( Jae-hun Ahn ),윤정숙 ( Jong-suk Yoon ),임경재 ( Kyoung Jae Lim ),최중대 ( Yong-chul Shin ),신용철 ( Changwon Lyou ),유창원 ( Joongdae Choi ) 한국농공학회 2006 한국농공학회 학술대회초록집 Vol.2006 No.-

The Doam-dam watershed, located at Kangwon Province, Korea, has been experiencing significant changes in land uses, conversion from forest to agricultural/urban areas, with human involvements. However, no thorough investigation of the landscape impacts of land use changes was performed at this watershed using scientific analytical tool. Thus, the FRAGSTATS model was utilized to quantitatively analyze the landscape impacts of forest fragmentation in this study. To provide the detailed explanations for 11 landscape indices considered in this study, two artificial and simplified landscapes, before and after fragmentations, were constructed. Using these 11 indices, the landscape impacts of forest fragmentation in 19 subwatersheds of the Doam-dam watershed were analyzed. The S1 subwatershed, one of 19 subwatersheds of the Doam-dam watershed, was found to have experienced the significant forest fragmentation from 1985 to 2000 based on landscape analysis. The results obtained in this study can be used to evaluate the water quality impacts of forest fragmentations/landuse changes at watershed scale level, and establish environment-friendly land use planning based on the results obtained using landscape analytical tool, FRAGSTATS.