Kyongsik Yun
Biographic Data
| ID | 7970252 |
|---|---|
| NAME | Kyongsik Yun |
| GIVEN NAMES | Kyongsik |
| FAMILY NAME | Yun |
| SIGNATURE | YUN K |
| AFFILIATIONS | California Institute of Technology |
| ORCID | 0000-0002-6103-7187 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 3 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2017 |
| LATEST PUBLICATION YEAR | 2020 |
| H-INDEX | 0 |
Identifying Schizophrenia Using Structural MRI With a Deep Learning Algorithm
The deep learning algorithm showed good performance in detecting schizophrenia and identified relevant structural features from structural brain MRI data; it had an acceptable classification performance in a separate group of patients at an earlier stage of the disease. Deep learning can be used to delineate the structural characteristics of schizophrenia and to provide supplementary diagnostic information in clinical settings
Association Between Macronutrients Intake and Depression in the United States and South Korea
Although the risk for depression appears to be related to daily dietary habits, how the proportion of major macronutrients affects the occurrence of depression remains largely unknown. This study aims to estimate the association between macronutrients (i.e., carbohydrate, protein, fat) and depression through national survey datasets from the United States and South Korea. Association between the prevalence of depression and each macronutrient was…
Classification of Suicide Attempts through a Machine Learning Algorithm Based on Multiple Systemic Psychiatric Scales
Classification and prediction of suicide attempts in high-risk groups is important for preventing suicide. The purpose of this study was to investigate whether the information from multiple clinical scales has classification power for identifying actual suicide attempts. Patients with depression and anxiety disorders ( N = 573) were included, and each participant completed 31 self-report psychiatric scales and questionnaires about their history o…
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Classification of Suicide Attempts through a Machine Learning Algorithm Based on Multiple Systemic Psychiatric Scales
Classification and prediction of suicide attempts in high-risk groups is important for preventing suicide. The purpose of this study was to investigate whether the information from multiple clinical scales has classification power for identifying actual suicide attempts. Patients with depression and anxiety disorders ( N = 573) were included, and each participant completed 31 self-report psychiatric scales and questionnaires about their history o…
Identifying Schizophrenia Using Structural MRI With a Deep Learning Algorithm
The deep learning algorithm showed good performance in detecting schizophrenia and identified relevant structural features from structural brain MRI data; it had an acceptable classification performance in a separate group of patients at an earlier stage of the disease. Deep learning can be used to delineate the structural characteristics of schizophrenia and to provide supplementary diagnostic information in clinical settings
Association Between Macronutrients Intake and Depression in the United States and South Korea
Although the risk for depression appears to be related to daily dietary habits, how the proportion of major macronutrients affects the occurrence of depression remains largely unknown. This study aims to estimate the association between macronutrients (i.e., carbohydrate, protein, fat) and depression through national survey datasets from the United States and South Korea. Association between the prevalence of depression and each macronutrient was…
Medicine (3 works) · Psychiatry (3 works) · Psychology (3 works) · Algorithm (2 works) · Artificial Intelligence (2 works) · Computer Science (2 works) · Depression (economics (2 works) · Machine learning (2 works) · Receiver operating characteristic (2 works) · Advanced MRI Techniques and Applications (1 work)