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150 results on '"Kwon, Soon-Sun"'

3. Validation and Reliability of a Smartphone Application for the International Prostate Symptom Score Questionnaire: A Randomized Repeated Measures Crossover Study

Author

Kim, Jae Heon, Kwon, Soon-Sun, Shim, Sung Ryul, Sun, Hwa Yeon, Ko, Young Myoung, Chun, Dong-Il, Yang, Won Jae, and Song, Yun Seob

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Public aspects of medicine, RA1-1270
Abstract

BackgroundSmartphone-based assessment may be a useful diagnostic and monitoring tool for patients. There have been many attempts to create a smartphone diagnostic tool for clinical use in various medical fields but few have demonstrated scientific validity. ObjectiveThe purpose of this study was to develop a smartphone application of the International Prostate Symptom Score (IPSS) and to demonstrate its validity and reliability. MethodsFrom June 2012 to May 2013, a total of 1581 male participants (≥40 years old), with or without lower urinary tract symptoms (LUTS), visited our urology clinic via the health improvement center at Soonchunhyang University Hospital (Republic of Korea) and were enrolled in this study. A randomized repeated measures crossover design was employed using a smartphone application of the IPSS and the conventional paper form of the IPSS. Paired t test under a hypothesis of non-inferior trial was conducted. For the reliability test, the intraclass correlation coefficient (ICC) was measured. ResultsThe total score of the IPSS (P=.289) and each item of the IPSS (P=.157-1.000) showed no differences between the paper version and the smartphone version of the IPSS. The mild, moderate, and severe LUTS groups showed no differences between the two versions of the IPSS. A significant correlation was noted in the total group (ICC=.935, P

Published
2014
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8. Enhanced trajectory data visualization: a dynamic time warping integrated t-SNE approach with real-data applications.

Author

Chung, Dahee, Kwon, Soon-Sun, and Ahn, Soohyun

Subjects
*TEMPORAL databases, *DATA structures, *DATA visualization
Abstract

AbstractIn this study, we focus on visualizing trajectory data, a type of data based on a series of temporal observations. We present an adapted version of t-distributed stochastic neighborhood embedding (t-SNE) tailored for trajectory data. This method is designed to preserve the inherent curved structure of trajectory data by incorporating a robust distance measure. Furthermore, it demonstrates the ability to maintain the data structure even in the presence of missing values at different time points. The performance of the proposed method is rigorously evaluated through a simulation study and demonstrated its effectiveness in visualizing two types of trajectory data, Gait data and NBA data. [ABSTRACT FROM AUTHOR]

Published
2024
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38. Classification of histogram-valued data with support histogram machines.

Author

Kang, Ilsuk, Park, Cheolwoo, Yoon, Young Joo, Park, Changyi, Kwon, Soon-Sun, and Choi, Hosik

Subjects
HISTOGRAMS, SUPPORT vector machines, CLASSIFICATION
Abstract

The current large amounts of data and advanced technologies have produced new types of complex data, such as histogram-valued data. The paper focuses on classification problems when predictors are observed as or aggregated into histograms. Because conventional classification methods take vectors as input, a natural approach converts histograms into vector-valued data using summary values, such as the mean or median. However, this approach forgoes the distributional information available in histograms. To address this issue, we propose a margin-based classifier called support histogram machine (SHM) for histogram-valued data. We adopt the support vector machine framework and the Wasserstein-Kantorovich metric to measure distances between histograms. The proposed optimization problem is solved by a dual approach. We then test the proposed SHM via simulated and real examples and demonstrate its superior performance to summary-value-based methods. [ABSTRACT FROM AUTHOR]

Published
2023
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