Zobrazeno 1 - 10
of 23
pro vyhledávání: '"Eun-Tae Jeon"'
Autor:
Yoon-Hee Choi, Dong Hyun Kim, Eun-Tae Jeon, Hyo Jin Lee, Tae Yun Park, Soon Ho Yoon, Kwang Nam Jin, Hyun Woo Lee
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-11 (2024)
Abstract Severe pneumonia results in high morbidity and mortality despite advanced treatments. This study investigates thoracic muscle mass from chest CT scans as a biomarker for predicting clinical outcomes in ICU patients with severe pneumonia. Ana
Externí odkaz:
https://doaj.org/article/b2592370e89b46b0956a9b518bd28129
Publikováno v:
Journal of Medicine and Life Science, Vol 20, Iss 4, Pp 141-157 (2023)
Stroke is a leading cause of disability and death. The condition requires prompt diagnosis and treatment. The quality of care provided to patients with stroke can vary depending on the availability of medical resources, which in turn, can affect prog
Externí odkaz:
https://doaj.org/article/80256a162a3546b58db23a1f2267ff85
Autor:
Eun-Tae Jeon, Hyo Jin Lee, Tae Yun Park, Kwang Nam Jin, Borim Ryu, Hyun Woo Lee, Dong Hyun Kim
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-12 (2023)
Abstract Conventional severity-of-illness scoring systems have shown suboptimal performance for predicting in-intensive care unit (ICU) mortality in patients with severe pneumonia. This study aimed to develop and validate machine learning (ML) models
Externí odkaz:
https://doaj.org/article/a412d6b46a9749c8857f549f20182060
Publikováno v:
Frontiers in Neurology, Vol 14 (2023)
BackgroundPrognostic prediction and the identification of prognostic factors are critical during the early period of atrial-fibrillation (AF)-related strokes as AF is associated with poor outcomes in stroke patients.MethodsTwo independent datasets, n
Externí odkaz:
https://doaj.org/article/ee17a6ab980b41d3a1d038fe926eda7f
Publikováno v:
Scientific Reports, Vol 12, Iss 1, Pp 1-10 (2022)
Abstract Despite the significance of predicting the prognosis of idiopathic sudden sensorineural hearing loss (ISSNHL), no predictive models have been established. This study used artificial intelligence to develop prognosis models to predict recover
Externí odkaz:
https://doaj.org/article/ebedba5138ae409e8e26ba2311a4bd2b
Autor:
Seong-Hwan Kim, Eun-Tae Jeon, Sungwook Yu, Kyungmi Oh, Chi Kyung Kim, Tae-Jin Song, Yong-Jae Kim, Sung Hyuk Heo, Kwang-Yeol Park, Jeong-Min Kim, Jong-Ho Park, Jay Chol Choi, Man-Seok Park, Joon-Tae Kim, Kang-Ho Choi, Yang Ha Hwang, Bum Joon Kim, Jong-Won Chung, Oh Young Bang, Gyeongmoon Kim, Woo-Keun Seo, Jin-Man Jung
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
Abstract We aimed to develop a novel prediction model for early neurological deterioration (END) based on an interpretable machine learning (ML) algorithm for atrial fibrillation (AF)-related stroke and to evaluate the prediction accuracy and feature
Externí odkaz:
https://doaj.org/article/7f7f84c61f6949d1b502eaea8df2f555
Publikováno v:
Journal of Personalized Medicine, Vol 12, Iss 10, p 1691 (2022)
According to the Korea Institute for Health and Social Affairs, in 2017, the elderly, aged 65 or older, had an average of 2.7 chronic diseases per person. The concern for the medical welfare of the elderly is increasing due to a low birth rate, an ag
Externí odkaz:
https://doaj.org/article/1c83c45725ea41f3a0570d21e66e61c5
Publikováno v:
Journal of Personalized Medicine, Vol 10, Iss 4, p 213 (2020)
According to recent studies, patients with COVID-19 have different feature characteristics on chest X-ray (CXR) than those with other lung diseases. This study aimed at evaluating the layer depths and degree of fine-tuning on transfer learning with a
Externí odkaz:
https://doaj.org/article/0a378ab6cce9440f85e167bcc1673318
Autor:
Eun-Tae Jeon1,2, Juhyun Song3 songcap97@hotmail.com, Dae Won Park4, Ki-Sun Lee2, Sejoong Ahn5, Joo Yeong Kim5, Jong-hak Park5, Sungwoo Moon5, Han-jin Cho5
Publikováno v:
Signa Vitae. Sep2023, Vol. 19 Issue 5, p112-124. 13p.
Autor:
Dong Hyun Kim, Jiwoon Seo, Ji Hyun Lee, Eun-Tae Jeon, DongYoung Jeong, Hee Dong Chae, Eugene Lee, Ji Hee Kang, Yoon-Hee Choi, Hyo Jin Kim, Jee Won Chai
Publikováno v:
Korean Journal of Radiology; Apr2024, Vol. 25 Issue 4, p363-373, 11p