Zobrazeno 1 - 10
of 32
pro vyhledávání: '"Junetae Kim"'
Publikováno v:
Journal of Medical Internet Research, Vol 26, p e59444 (2024)
BackgroundDigital health care apps, including digital therapeutics, have the potential to increase accessibility and improve patient engagement by overcoming the limitations of traditional facility-based medical treatments. However, there are no esta
Externí odkaz:
https://doaj.org/article/93a1420d58ef49a5bde9d4f399a7c15f
Autor:
Tao Thi Tran, Jeonghee Lee, Madhawa Gunathilake, Junetae Kim, Sun-Young Kim, Hyunsoon Cho, Jeongseon Kim
Publikováno v:
Frontiers in Oncology, Vol 13 (2023)
BackgroundLittle is known about applying machine learning (ML) techniques to identify the important variables contributing to the occurrence of gastrointestinal (GI) cancer in epidemiological studies. We aimed to compare different ML models to a Cox
Externí odkaz:
https://doaj.org/article/d6e2cab4bc7c48c591a516e0640e39b4
Autor:
Young Joo Lee, Young Sol Hwang, Junetae Kim, Sei-Hyun Ahn, Byung Ho Son, Hee Jeong Kim, Beom Seok Ko, Jisun Kim, Il Yong Chung, Jong Won Lee, Sae Byul Lee
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
Abstract We aimed to develop a prediction MammaPrint (MMP) genomic risk assessment nomogram model for hormone-receptor positive (HR+) and human epidermal growth factor receptor-2 negative (HER2–) breast cancer and minimal axillary burden (N0-1) tum
Externí odkaz:
https://doaj.org/article/4b3a0ab2ac874f739cb719bdd370d43a
Monitoring arterial blood pressure (ABP) in anesthetized patients is crucial for preventing hypotension, which can lead to adverse clinical outcomes. Thus, several efforts have been made to develop an artificial intelligence-based hypotension predict
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a98c73e927174260d822392b27d2f5f4
https://doi.org/10.36227/techrxiv.21748085
https://doi.org/10.36227/techrxiv.21748085
Autor:
Sung-Hoon Kim, Junetae Kim
Monitoring arterial blood pressure (ABP) in anesthetized patients is crucial for preventing hypotension, which can lead to adverse clinical outcomes. Thus, several efforts have been made to develop an artificial intelligence-based hypotension predict
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cfda8b00696f44bf2edff2c64a0b1b95
https://doi.org/10.36227/techrxiv.21748085.v1
https://doi.org/10.36227/techrxiv.21748085.v1
Publikováno v:
2022 IEEE International Conference on Big Data (Big Data).
BACKGROUND Intraoperative hypotension (IOH) is associated with an increased risk of postoperative complications. Therefore, in recent years, various models for IOH prediction based on high-dimensional signal data have been developed. Given that the a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::89f653a003ffdf647b708a2dc0b6e97e
https://doi.org/10.2196/preprints.43831
https://doi.org/10.2196/preprints.43831
Autor:
Eugene Hwang, Hee-Sun Park, Hyun-Seok Kim, Jin-Young Kim, Hanseok Jeong, Junetae Kim, Sung-Hoon Kim
Publikováno v:
Artificial Intelligence in Medicine. :102569
Publikováno v:
Information Systems Research. 32:497-516
Research Spotlight
Autor:
Sae Byul Lee1, Junetae Kim2, Guiyun Sohn1, Jisun Kim1, Il Yong Chung1, Hee Jeong Kim1, Beom Seok Ko1, Byung Ho Son1, Sei-Hyun Ahn1, Jong Won Lee1 jjjongwr@hanmail.net, Kyung Hae Jung3 khjung@amc.seoul.kr
Publikováno v:
Cancer Research & Treatment. Jul2019, Vol. 51 Issue 3, p1073-1085. 13p.