Seok‐Jae Heo
Biographic Data
| ID | 7870950 |
|---|---|
| NAME | Seok‐Jae Heo |
| GIVEN NAMES | Seok‐Jae |
| FAMILY NAME | Heo |
| SIGNATURE | HEO S J |
| AFFILIATIONS | Yonsei University |
| ORCID | 0000-0002-8764-7995 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 3 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2019 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 0 |
Socioeconomic disparities and cardio-cerebrovascular diseases
Background: Although socioeconomic status (SES) is considered a risk factor for cardio-cerebrovascular diseases (CCVDs), few studies have examined this association. In this cross-sectional study, we aimed to assess the prevalence and trends of CCVDs across different SES groups over a 12-year period in a representative Korean population. Methods: We analysed 47 745 economically active adults aged ≥30 and 30 increased, whereas CCVD prevalence did n…
The Feasibility of Cervical Elastography in Predicting Preterm Delivery in Singleton Pregnancy with Short Cervix Following Progesterone Treatment
Previous studies demonstrated an association between cervical strain and risk of spontaneous preterm delivery (sPTD). The present study aimed to assess the efficacy of elastography in predicting sPTD at p = 0.04, 0.012 and 0.026, respectively). A combination of CL at diagnosis, PP0 IOS and PP1 EOS showed a significantly higher area under the receiver operating characteristic curve (0.858) than that of CL alone ( p = 0.041). In women with singleto…
Deep Learning Algorithms with Demographic Information Help to Detect Tuberculosis in Chest Radiographs in Annual Workers’ Health Examination Data
We aimed to use deep learning to detect tuberculosis in chest radiographs in annual workers' health examination data and compare the performances of convolutional neural networks (CNNs) based on images only (I-CNN) and CNNs including demographic variables (D-CNN). The I-CNN and D-CNN models were trained on 1000 chest X-ray images, both positive and negative, for tuberculosis. Feature extraction was conducted using VGG19, InceptionV3, ResNet50, De…
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Deep Learning Algorithms with Demographic Information Help to Detect Tuberculosis in Chest Radiographs in Annual Workers’ Health Examination Data
We aimed to use deep learning to detect tuberculosis in chest radiographs in annual workers' health examination data and compare the performances of convolutional neural networks (CNNs) based on images only (I-CNN) and CNNs including demographic variables (D-CNN). The I-CNN and D-CNN models were trained on 1000 chest X-ray images, both positive and negative, for tuberculosis. Feature extraction was conducted using VGG19, InceptionV3, ResNet50, De…
The Feasibility of Cervical Elastography in Predicting Preterm Delivery in Singleton Pregnancy with Short Cervix Following Progesterone Treatment
Previous studies demonstrated an association between cervical strain and risk of spontaneous preterm delivery (sPTD). The present study aimed to assess the efficacy of elastography in predicting sPTD at p = 0.04, 0.012 and 0.026, respectively). A combination of CL at diagnosis, PP0 IOS and PP1 EOS showed a significantly higher area under the receiver operating characteristic curve (0.858) than that of CL alone ( p = 0.041). In women with singleto…
Socioeconomic disparities and cardio-cerebrovascular diseases
Background: Although socioeconomic status (SES) is considered a risk factor for cardio-cerebrovascular diseases (CCVDs), few studies have examined this association. In this cross-sectional study, we aimed to assess the prevalence and trends of CCVDs across different SES groups over a 12-year period in a representative Korean population. Methods: We analysed 47 745 economically active adults aged ≥30 and 30 increased, whereas CCVD prevalence did n…
Medicine (3 works) · Internal Medicine (2 works) · Radiology (2 works) · Receiver operating characteristic (2 works) · Algorithm (1 work) · Area under the curve (1 work) · Artificial Intelligence (1 work) · Biology (1 work) · Cardiovascular Health and Risk Factors (1 work) · Cervix (1 work)