A5058073637- ECG Monitoring and Analysis
- Machine Learning in Healthcare
- Dementia and Cognitive Impairment Research
- Rheumatoid Arthritis Research and Therapies
- Bone health and osteoporosis research
- Bone and Joint Diseases
- Artificial Intelligence in Healthcare
- Digital Radiography and Breast Imaging
- Healthcare Systems and Public Health
- Neural Networks and Applications
- Natural Language Processing Techniques
- Text and Document Classification Technologies
- Advanced Semiconductor Detectors and Materials
- Advanced X-ray and CT Imaging
- Functional Brain Connectivity Studies
- Solid State Laser Technologies
- Electrostatic Discharge in Electronics
- Acute Ischemic Stroke Management
- Neurological Disease Mechanisms and Treatments
- Machine Learning and Data Classification
- Atrial Fibrillation Management and Outcomes
- Topic Modeling
- Cardiac electrophysiology and arrhythmias
- Aging, Elder Care, and Social Issues
- Image and Signal Denoising Methods
Kangwon National University
2018-2024
Nation University
2023
Convergence
2023
Korea Institute of Science and Technology
1999
Machine learning (ML) and large-scale big data are key factors in developing an accurate prediction model for cardiovascular disease (CVD). Although the CVD risk often depends on race ethnicity, most previous studies considered only US or European populations prediction. In this work, to complement researches, we analyzed Korean National Health Insurance Service-National Sample Cohort (KNHSC) studied characteristics of ML predicting risk. More specifically, assessed effectiveness various...
The prevalence of Alzheimer's dementia (AD) is increasing as populations age, causing immense suffering for patients, families, and communities. Unfortunately, no treatments this neurodegenerative disease have been established. Predicting AD therefore becoming more important, because early diagnosis the best way to prevent its onset delay progression.
Abstract Osteoporosis is a serious health concern in patients with rheumatoid arthritis (RA). Machine learning (ML) models have been increasingly incorporated into various clinical practices, including disease classification, risk prediction, and treatment response. However, only few studies focused on predicting osteoporosis using ML RA. We aimed to develop an model predict representative Korean RA cohort database. The KORean Observational study Network for Arthritis (KORONA) database,...
Abstract Osteoporosis is a serious health concern in patients with rheumatoid arthritis (RA). Machine learning (ML) models have been increasingly incorporated into various clinical practices, including disease classification, risk prediction, and treatment response. However, only few studies focused on predicting osteoporosis using ML RA. We aimed to develop an model predict representative Korean RA cohort database. The KORean Observational study Network for Arthritis (KORONA) database,...
Background and aims It is difficult to document atrial fibrillation (AF) on ECG in patients with non-persistent (non-PeAF). There limited understanding of whether an AI prediction algorithm could predict the occurrence non-PeAF from information normal sinus rhythm (SR) a 12-lead ECG. This study aimed derive precise predictive model for screening using SR within 4 weeks. Methods retrospective cohort included aged 18 99 standard (10 seconds) Ewha Womans University Medical Center 3 years. Data...
Identifying biomarkers for predicting progression to dementia in patients with mild cognitive impairment (MCI) is crucial. To this end, the comprehensive visual rating scale (CVRS), which based on magnetic resonance imaging (MRI), was developed assessment of structural changes brains MCI. This study aimed investigate use CVRS score MCI over a 2-year follow-up period using various machine learning (ML) algorithms. We included 197 who were followed up more than once. The data used obtained...
최근 온라인 쇼핑몰을 이용하는 소비자가 증가하면서 쇼핑 거래를 비롯하여 쇼핑몰에 등록되는 상품의 수 역시 매우 빠르게 증가하고 있다. 하지만 사람이 직접 수많은 이름만 보고 정확하고 범주를 분류하기는 쉽지 않다. 본 논문에서는 기계학습을 이용하여 상품명 정보 데이터를 효과적으로 분류하는 모델을 구현하였다. 상품 정보를 분류하기 위해 데이터 전처리, 형태소 분석, 단어 임베딩, GRU 모델 등을 활용하였다. 특히, 사전 학습된 임베딩 사용하지 않고, 데이터의 특징을 분석하고 이를 바탕으로 학습시켰으며, 시각화를 통해 모델이 적절하게 학습하였음을 확인하였다. 구현한 모델의 성능을 측정하기 식품, 출산/육아용품, 생활/취미용품 총 세 가지 범주에 대해 다양한 상품명을 실험을 수행하였으며, 각 범주별로 AUC 값이 0.91, 0.82, 0.88로 비교적 분류가 잘 수행됨을
Second harmonic generation (SHG) of 532-nm radiation from 1064-nm in LiNbO<SUB>3</SUB> doped with 0.6 mol% MgO has been investigated by using pulsed Nd:YAG lasers whose pulse duration is less than 25 nanoseconds. Efficient and noncritically phase-matched SHG achieved at room temperature without any severe photorefractive problem. conversion efficiency 50% was typically obtained a 9.5-mm-long crystal fundamental peak-power density 22 MW/cm<SUP>2</SUP> 0.8 GW/cm<SUP>2</SUP> when the laser are...