Inkyung Jung
연세대학교 의과대학 의학과 · 의학
이 교수의 연구실은 공간통계 기반 질병 클러스터 탐지 기법에 중점을 두고 있으며, 특히 정량적 데이터뿐 아니라 순서형, 다항형, 연속형 데이터 등 다양한 유형의 의료 데이터를 효과적으로 분석할 수 있는 공간스캔 통계 방법을 개발하고 있습니다. 특히, 계층적 구조를 가진 약물 부작용 데이터나 질병 유형 분포의 공간적 비정상성을 탐지하는 데 응용 가능한 고도화된 모델링 기법을 연구하고 있습니다. 또한, 공변량 보정이 가능한 일반화선형모형 기반의 공간스캔 통계 기법을 통해 실제 보건 데이터의 복잡성을 반영한 분석이 가능하도록 기초 이론과 응용을 동시에 발전시키고 있습니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
Spatial scan statistics are widely used for count data to detect geographical disease clusters of high or low incidence, mortality or prevalence and to evaluate their statistical significance. Some data are ordinal or continuous in nature, however, so that it is necessary to dichotomize the data to use a traditional scan statistic for count data. There is then a loss of information and the choice of cut-off point is often arbitrary. In this paper, we propose a spatial scan statistic for ordinal
As a geographical cluster detection analysis tool, the spatial scan statistic has been developed for different types of data such as Bernoulli, Poisson, ordinal, exponential and normal. Another interesting data type is multinomial. For example, one may want to find clusters where the disease-type distribution is statistically significantly different from the rest of the study region when there are different types of disease. In this paper, we propose a spatial scan statistic for such data, which
There are several different proposed data mining methods for the postmarketing surveillance of drug safety. Adverse events are often classified into a hierarchical structure. Our objective was to compare the performance of several of these different data mining methods for adverse drug events data with a hierarchical structure. We generated datasets based on the World Health Organization's Adverse Reaction Terminology (WHO-ART) hierarchical structure. We evaluated different data mining methods f
The spatial scan statistic proposed by Kulldorff (Commun. Statist.-Theory Methods 1997; 26:1481-1496) is one of the most widely used methods for detecting spatial clusters and evaluating their statistical significance. However, it is not fully capable of adjusting for all types of confounding covariates. In this article, a generalized linear models (GLM) approach to construct spatial scan statistics, which is readily in a form for covariate adjustment, is proposed. Using GLM, spatial scan statis
The spatial scan statistic is an important tool for spatial cluster detection. There have been numerous studies on scanning window shapes. However, little research has been done on the maximum scanning window size or maximum reported cluster size. Recently, Han et al. proposed to use the Gini coefficient to optimize the maximum reported cluster size. However, the method has been developed and evaluated only for the Poisson model. We adopt the Gini coefficient to be applicable to the spatial scan
In a recent report, Roehle et al (2008) described the global microRNA (miRNA) expression signature of B-cell lymphomas. In that study, the authors were also able to investigate the impact of miRNAs expression on the survival of diffuse large B-cell lymphomas (DLBCL). However, although several miRNAs segregated with outcome in their series, expression of the oncogenic MIRN155 played no role on the overall or event-free survival of DLBCL patients. MiRNAs are abundant non-protein coding RNAs that a
The proposed nonparametric spatial scan statistic is therefore an excellent alternative to the normal model for continuous data and is especially useful for data following skewed or heavy-tailed distributions.
