Haewon Byeon
Biographic Data
| ID | 4990480 |
|---|---|
| NAME | Haewon Byeon |
| GIVEN NAMES | Haewon |
| FAMILY NAME | Byeon |
| SIGNATURE | BYEON H |
| AFFILIATIONS | Inje University |
| ORCID | 0000-0002-3363-390X |
| VERIFIED | Yes |
| TOTAL WORKS | 13 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 13 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2019 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Defying the economic odds
For individuals living with severe mental disorders (SMD) such as schizophrenia, exclusion from the labor market creates a secondary pathology: the erosion of identity, autonomy, and social purpose. Although many people with SMD want to work, competitive employment remains difficult to attain. The Individual Placement and Support (IPS) model has challenged the assumption that illness severity alone explains poor labor-market participation in the …
Deep Learning Model for Interpretability and Explainability of Aspect-Level Sentiment Analysis Based on Social Media
The interactive attention graph convolution network (IAGCN), a novel model proposed in this article, will revolutionize aspect-level sentiment analysis (SA). IAGCN effectively addresses these key features, in contrast to prior research that ignored the meaning of aspect terms and their relationship with context. The model combines a modified dynamic weighting layer with bidirectional long short-term memory (BiLSTM) to accurately acquire context. …
A hybrid self-supervised model predicting life satisfaction in South Korea
Objective: Life satisfaction pertains to an individual's subjective evaluation of their life quality, grounded in their personal criteria. It stands as a crucial cognitive aspect of subjective wellbeing, offering a reliable gauge of a person's comprehensive wellbeing status. In this research, our objective is to develop a hybrid self-supervised model tailored for predicting individuals' life satisfaction in South Korea. Methods: We employed the B…
Few-shot learning-based human behavior recognition model
Developing a nomogram for predicting depression in diabetic patients after Covid-19 using machine learning
Objective: This study identified major risk factors for depression in community diabetic patients using machine learning techniques and developed predictive models for predicting the high-risk group for depression in diabetic patients based on multiple risk factors. Methods: This study analyzed 26,829 adults living in the community who were diagnosed with diabetes by a doctor. The prevalence of a depressive disorder was the dependent variable in …
Development of Keyword Trend Prediction Models for Obesity Before and After the Covid-19 Pandemic Using RNN and LSTM
The Korea National Health and Nutrition Examination Survey (2020) reported that the prevalence of obesity (≥19 years old) was 31.4% in 2011, but it increased to 33.8% in 2019 and 38.3% in 2020, which confirmed that it increased rapidly after the outbreak of COVID-19. Obesity increases not only the risk of infection with COVID-19 but also severity and fatality rate after being infected with COVID-19 compared to people with normal weight or underwe…
Developing a Predictive Model for Depressive Disorders Using Stacking Ensemble and Naive Bayesian Nomogram
This study provided baseline data for preventing depression in female older adults living alone by understanding the degree of their depressive disorders and factors affecting these depressive disorders by analyzing epidemiological survey data representing South Koreans. To achieve the study objective, this study explored the main risk factors of depressive disorders using the stacking ensemble machine technique. Moreover, this study developed a …
ICF-Based Job Performance Predictors for South Korean Industrial Accident Workers
Since workers who have experienced industrial accidents may have a physical impairment, their workload is very likely to be less than before the industrial accident. This epidemiological study identified ICF-based predictive factors affecting the work performance of South Korean workers who were economically active after undergoing medical treatment (rehabilitation) related to injuries caused by industrial accidents by using the Panel Study of Wo…
Exploring Factors for Predicting Anxiety Disorders of the Elderly Living Alone in South Korea Using Interpretable Machine Learning
This epidemiological study aimed to develop an X-AI that could explain groups with a high anxiety disorder risk in old age. To achieve this objective, (1) this study explored the predictors of senile anxiety using base models and meta models. (2) This study presented decision tree visualization that could help psychiatric consultants and primary physicians easily interpret the path of predicting high-risk groups based on major predictors derived …
Predicting the Severity of Parkinson’s Disease Dementia by Assessing the Neuropsychiatric Symptoms with an SVM Regression Model
In this study, we measured the convergence rate using the mean-squared error (MSE) of the standardized neuropsychological test to determine the severity of Parkinson's disease dementia (PDD), which is based on support vector machine (SVM) regression (SVR) and present baseline data in order to develop a model to predict the severity of PDD. We analyzed 328 individuals with PDD who were 60 years or older. To identify the SVR with the best predictio…
Is the Random Forest Algorithm Suitable for Predicting Parkinson’s Disease with Mild Cognitive Impairment out of Parkinson’s Disease with Normal Cognition
Because it is possible to delay the progression of dementia if it is detected and treated in an early stage, identifying mild cognitive impairment (MCI) is an important primary goal of dementia treatment. The objectives of this study were to develop a random forest-based Parkinson's disease with mild cognitive impairment (PD-MCI) prediction model considering health behaviors, environmental factors, medical history, physical functions, depression,…
Relationship between Physical Activity Level and Depression of Elderly People Living Alone
The results of our study implied that persistent flexibility exercise might be more effective to maintain a healthy mental status than muscular strength exercise. A longitudinal study is required to prove the causal relationship between physical activity and depression in the old age
Predicting the Swallow-Related Quality of Life of the Elderly Living in a Local Community Using Support Vector Machine
Background and Objectives: This study developed a support vector machine (SVM) algorithm-based prediction model with considering influence factors associated with the swallowing quality-of-life as the predictor variables and provided baseline information for enhancing the swallowing quality of elderly people's lives in the future. Methods and Material: This study sampled 142 elderly people equal to or older than 65 years old who were using a seni…
No prominent works on this page.
Relationship between Physical Activity Level and Depression of Elderly People Living Alone
The results of our study implied that persistent flexibility exercise might be more effective to maintain a healthy mental status than muscular strength exercise. A longitudinal study is required to prove the causal relationship between physical activity and depression in the old age
Predicting the Swallow-Related Quality of Life of the Elderly Living in a Local Community Using Support Vector Machine
Background and Objectives: This study developed a support vector machine (SVM) algorithm-based prediction model with considering influence factors associated with the swallowing quality-of-life as the predictor variables and provided baseline information for enhancing the swallowing quality of elderly people's lives in the future. Methods and Material: This study sampled 142 elderly people equal to or older than 65 years old who were using a seni…
Is the Random Forest Algorithm Suitable for Predicting Parkinson’s Disease with Mild Cognitive Impairment out of Parkinson’s Disease with Normal Cognition
Because it is possible to delay the progression of dementia if it is detected and treated in an early stage, identifying mild cognitive impairment (MCI) is an important primary goal of dementia treatment. The objectives of this study were to develop a random forest-based Parkinson's disease with mild cognitive impairment (PD-MCI) prediction model considering health behaviors, environmental factors, medical history, physical functions, depression,…
Exploring Factors for Predicting Anxiety Disorders of the Elderly Living Alone in South Korea Using Interpretable Machine Learning
This epidemiological study aimed to develop an X-AI that could explain groups with a high anxiety disorder risk in old age. To achieve this objective, (1) this study explored the predictors of senile anxiety using base models and meta models. (2) This study presented decision tree visualization that could help psychiatric consultants and primary physicians easily interpret the path of predicting high-risk groups based on major predictors derived …
Predicting the Severity of Parkinson’s Disease Dementia by Assessing the Neuropsychiatric Symptoms with an SVM Regression Model
In this study, we measured the convergence rate using the mean-squared error (MSE) of the standardized neuropsychological test to determine the severity of Parkinson's disease dementia (PDD), which is based on support vector machine (SVM) regression (SVR) and present baseline data in order to develop a model to predict the severity of PDD. We analyzed 328 individuals with PDD who were 60 years or older. To identify the SVR with the best predictio…
Development of Keyword Trend Prediction Models for Obesity Before and After the Covid-19 Pandemic Using RNN and LSTM
The Korea National Health and Nutrition Examination Survey (2020) reported that the prevalence of obesity (≥19 years old) was 31.4% in 2011, but it increased to 33.8% in 2019 and 38.3% in 2020, which confirmed that it increased rapidly after the outbreak of COVID-19. Obesity increases not only the risk of infection with COVID-19 but also severity and fatality rate after being infected with COVID-19 compared to people with normal weight or underwe…
Developing a Predictive Model for Depressive Disorders Using Stacking Ensemble and Naive Bayesian Nomogram
This study provided baseline data for preventing depression in female older adults living alone by understanding the degree of their depressive disorders and factors affecting these depressive disorders by analyzing epidemiological survey data representing South Koreans. To achieve the study objective, this study explored the main risk factors of depressive disorders using the stacking ensemble machine technique. Moreover, this study developed a …
ICF-Based Job Performance Predictors for South Korean Industrial Accident Workers
Since workers who have experienced industrial accidents may have a physical impairment, their workload is very likely to be less than before the industrial accident. This epidemiological study identified ICF-based predictive factors affecting the work performance of South Korean workers who were economically active after undergoing medical treatment (rehabilitation) related to injuries caused by industrial accidents by using the Panel Study of Wo…
Developing a nomogram for predicting depression in diabetic patients after Covid-19 using machine learning
Objective: This study identified major risk factors for depression in community diabetic patients using machine learning techniques and developed predictive models for predicting the high-risk group for depression in diabetic patients based on multiple risk factors. Methods: This study analyzed 26,829 adults living in the community who were diagnosed with diabetes by a doctor. The prevalence of a depressive disorder was the dependent variable in …
A hybrid self-supervised model predicting life satisfaction in South Korea
Objective: Life satisfaction pertains to an individual's subjective evaluation of their life quality, grounded in their personal criteria. It stands as a crucial cognitive aspect of subjective wellbeing, offering a reliable gauge of a person's comprehensive wellbeing status. In this research, our objective is to develop a hybrid self-supervised model tailored for predicting individuals' life satisfaction in South Korea. Methods: We employed the B…
Few-shot learning-based human behavior recognition model
Deep Learning Model for Interpretability and Explainability of Aspect-Level Sentiment Analysis Based on Social Media
The interactive attention graph convolution network (IAGCN), a novel model proposed in this article, will revolutionize aspect-level sentiment analysis (SA). IAGCN effectively addresses these key features, in contrast to prior research that ignored the meaning of aspect terms and their relationship with context. The model combines a modified dynamic weighting layer with bidirectional long short-term memory (BiLSTM) to accurately acquire context. …
Defying the economic odds
For individuals living with severe mental disorders (SMD) such as schizophrenia, exclusion from the labor market creates a secondary pathology: the erosion of identity, autonomy, and social purpose. Although many people with SMD want to work, competitive employment remains difficult to attain. The Individual Placement and Support (IPS) model has challenged the assumption that illness severity alone explains poor labor-market participation in the …
Medicine (10 works) · Computer Science (8 works) · Artificial Intelligence (7 works) · Machine learning (7 works) · Internal Medicine (6 works) · Disease (4 works) · Gerontology (4 works) · Health and Wellbeing Research (4 works) · Psychiatry (4 works) · Support vector machine (4 works)