Jee Eun Park
Seoul National University · Medicine
About the Lab
Professor Jee Eun Park's research lab specializes in biomedical imaging and data-driven health analytics, focusing on radiomics, medical image analysis, and the integration of environmental and physiological factors in disease prediction. The lab develops advanced computational models to improve diagnostic accuracy in oncology—particularly in distinguishing treatment-related changes from tumor progression in glioblastoma—while also investigating the impact of climate variables on infectious disease incidence. Additionally, the lab explores physiological biomarkers such as heart rate variability to assess emotional states, bridging biomedicine with behavioral science. The overarching goal is to enhance clinical decision-making through reproducible, generalizable, and patient-specific imaging and data science approaches.
Research Overview
Research Output Trend
Figures are computed from collected data and may differ slightly.
Selected Papers
15Radiomics, which involves the use of high-dimensional quantitative imaging features for predictive purposes, is a powerful tool for developing and testing medical hypotheses. Radiologic and statistical challenges in radiomics include those related to the reproducibility of imaging data, control of overfitting due to high dimensionality, and the generalizability of modeling. The aims of this review article are to clarify the distinctions between radiomics features and other omics and imaging data
The transmissibility and severity of MERS differed by outbreak region and patient characteristics. Further studies assessing the risk of MERS should consider these factors.
BACKGROUND: The effect of temperature and humidity on the incidence of influenza may differ by climate region. In addition, the effect of diurnal temperature range on influenza incidence is unclear, according to previous study findings. OBJECTIVES: The aim of this study was to analyze the effects of temperature, humidity, and diurnal temperature range on the incidence of influenza in Seoul, Republic of Korea, which is located in a temperate region. METHODS: We used Korean National Health insuran
BACKGROUND: Pseudoprogression is a diagnostic challenge in early posttreatment glioblastoma. We therefore developed and validated a radiomics model using multiparametric MRI to differentiate pseudoprogression from early tumor progression in patients with glioblastoma. METHODS: The model was developed from the enlarging contrast-enhancing portions of 61 glioblastomas within 3 months after standard treatment with 6472 radiomic features being obtained from contrast-enhanced T1-weighted imaging, flu
Because human emotion varies greatly among individuals and is a qualitative factor, measuring it with any degree of accuracy is very difficult. Heart rate variability (HRV), which is used in evaluations of the autonomic nervous system (ANS), is used to evaluate human emotions. This study examines the validity of HRV as a tool to evaluate emotions using the International Affective Picture System (IAPS). For experimentation, five photos were selected for each of the categories of "happy," "unhappy
Purpose Innovative consumers are an important market segment. This paper seeks to investigate whether consumers' innate innovativeness is associated with their shopping styles. Specifically, it aims to explore the relationship between two types of innovativeness – sensory and cognitive – and consumer shopping styles. Design/methodology/approach The paper integrates the consumer innovativeness and consumer shopping styles literature. It is built on the premise that if consumer innovativeness is r
Background: Radiomics is a rapidly growing field in neuro-oncology, but studies have been limited to conventional MRI, and external validation is critically lacking. We evaluated technical feasibility, diagnostic performance, and generalizability of a diffusion radiomics model for identifying atypical primary central nervous system lymphoma (PCNSL) mimicking glioblastoma. Methods: A total of 1618 radiomics features were extracted from diffusion and conventional MRI from 112 patients (training se
We aimed to establish a high-performing and robust classification strategy, using magnetic resonance imaging (MRI), along with combinations of feature extraction and selection in human and machine learning using radiomics or deep features by employing a small dataset. Using diffusion and contrast-enhanced T1-weighted MR images obtained from patients with glioblastomas and primary central nervous system lymphomas, classification task was assigned to a combination of radiomic features and (1) supe
BACKGROUND: To evaluate radiomics analysis in neuro-oncologic studies according to a radiomics quality score (RQS) system to find room for improvement in clinical use. METHODS: Pubmed and Embase were searched up the terms radiomics or radiogenomics and gliomas or glioblastomas until February 2019. From 189 articles, 51 original research articles reporting the diagnostic, prognostic, or predictive utility were selected. The quality of the methodology was evaluated according to the RQS. The adhere
Research Areas
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