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Hypertension Occurrence Analysis using a Bayesian Network
Junghye Lee(이정혜),Wonji Lee(이원지),Hyeseon Lee(이혜선),Chi-Hyuck Jun(전치혁) 한국경영과학회 2014 한국경영과학회 학술대회논문집 Vol.2014 No.5
The Bayesian network (BN) is a useful method for modeling healthcare issues since a BN can graphically represent causal relationships among variables and provide its probabilistic information,. In this study, we apply a BN method to hypertension occurrence analysis. This study used the National Health Insurance Corporation (NHIC) database from 2002 to 2010 which contains more than 100,000 cases of personal medical examinations in Korea. We investigate the causality for hypertension occurrence by a structure learning step, and then evaluate the performance to predict hypertension occurrence through parameter learning and inference steps. It is shown that the BN outperforms other prediction methods such as logistic regression, naive Bayes and support vector machine in terms of sensitivity. In addition, the BN has advantages in interpreting which variables affect the hypertension occurrence and how they are related to each other.
Hypertension Occurrence Analysis using a Bayesian Network
Junghye Lee(이정혜),Wonji Lee(이원지),Hyeseon Lee(이혜선),Chi-Hyuck Jun(전치혁) 대한산업공학회 2014 대한산업공학회 춘계학술대회논문집 Vol.2014 No.5
The Bayesian network (BN) is a useful method for modeling healthcare issues since a BN can graphically represent causal relationships among variables and provide its probabilistic information,. In this study, we apply a BN method to hypertension occurrence analysis. This study used the National Health Insurance Corporation (NHIC) database from 2002 to 2010 which contains more than 100,000 cases of personal medical examinations in Korea. We investigate the causality for hypertension occurrence by a structure learning step, and then evaluate the performance to predict hypertension occurrence through parameter learning and inference steps. It is shown that the BN outperforms other prediction methods such as logistic regression, naive Bayes and support vector machine in terms of sensitivity. In addition, the BN has advantages in interpreting which variables affect the hypertension occurrence and how they are related to each other.
Risk Prediction of Hypertension Complicates using Classification Techniques
Wonji Lee(이원지),Junghye Lee(이정혜),Hyeseon Lee(이혜선),Chi-Hyuck Jun(전치혁),Il-su Park 한국경영과학회 2014 한국경영과학회 학술대회논문집 Vol.2014 No.5
A hypertension complications is one of the sources causing the national medical expenditures to increase. We aim to score the risk of hypertension complications for hypertension patients, using national healthcare database established by Korean National Health Insurance Corporation. We apply classification techniques such as logistic regression, linear discriminant analysis and classification and regression tree to score the risk of hypertension complication onset and also compare the performance of those methods. These three methods seem to perform similarly although the logistic regression performs better than others marginally. This study is meaningful in that the database used is a representative sample for the whole nation.
Risk Prediction of Hypertension Complicates using Classification Techniques
Wonji Lee(이원지),Junghye Lee(이정혜),Hyeseon Lee(이혜선),Chi-Hyuck Jun(전치혁),Il-su Park 대한산업공학회 2014 대한산업공학회 춘계학술대회논문집 Vol.2014 No.5
A hypertension complications is one of the sources causing the national medical expenditures to increase. We aim to score the risk of hypertension complications for hypertension patients, using national healthcare database established by Korean National Health Insurance Corporation. We apply classification techniques such as logistic regression, linear discriminant analysis and classification and regression tree to score the risk of hypertension complication onset and also compare the performance of those methods. These three methods seem to perform similarly although the logistic regression performs better than others marginally. This study is meaningful in that the database used is a representative sample for the whole nation.
Word2vec based Latent Semantic Analysis (W2V-LSA): 새로운 토픽 모델링을 통한 블록체인 기술 연구 트렌드 분석
김수현(Suhyeon Kim),박해청(Haecheong Park),이정혜(Junghye Lee) 대한산업공학회 2018 대한산업공학회 추계학술대회논문집 Vol.2018 No.11
It is an urgent task to analyze trends in Blockchain technology to help establish action plans based on Blockchain, which is one of the core technologies in Industry 4.0. This study provides the trend analysis on Blockchain based on topic modeling of text mining for 231 abstracts of Blockchain-related papers published over the past five years. We developed a new topic modeling method called Word2vec-based Latent Semantic Analysis (W2V-LSA), which is based on Word2vec and Spherical k-means clustering to capture the context of corpus in a better representation. We used W2V-LSA to perform the annual trend analysis of Blockchain research by country and compared the results with Probabilistic LSA. We demonstrated the usefulness of W2V-LSA in terms of accuracy and diversity of the topics captured for the documents. It is believed that W2V-LSA will be a useful alternative for better topic modeling and the trend analysis of W2V-LSA will provide insight and show the direction for the future research on Blockchain technology.