Zobrazeno 1 - 10 of 28 pro vyhledávání: '"Hyon Hee Kim"'
1
Akademický článek
Aspirin, metformin, and statin use on the risk of gastric cancer: A nationwide population‐based cohort study in Korea with systematic review and meta‐analysis
Autor: Seung In Seo, Chan Hyuk Park, Tae Jun Kim, Chang Seok Bang, Jae Young Kim, Kyung Joo Lee, Jinseob Kim, Hyon Hee Kim, Seng Chan You, Woon Geon Shin
Publikováno v: Cancer Medicine, Vol 11, Iss 4, Pp 1217-1231 (2022)
Abstract Background/Aims Although several chemopreventive drugs against gastric cancer have been proposed, their effects have not been fully evaluated. We examined the impacts of aspirin, metformin, and statin use on gastric cancer development in a p
Externí odkaz: https://doaj.org/article/cf3670fbb7ca4173b4e65c6f77251298
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2
Akademický článek
A Deep Recurrent Neural Network-Based Explainable Prediction Model for Progression from Atrophic Gastritis to Gastric Cancer
Autor: Hyon Hee Kim, Young Seo Lim, Seung-In Seo, Kyung Joo Lee, Jae Young Kim, Woon Geon Shin
Publikováno v: Applied Sciences, Vol 11, Iss 13, p 6194 (2021)
Gastric cancer is the fifth most common cancer type worldwide and one of the most frequently diagnosed cancers in South Korea. In this study, we propose DeepPrevention, which comprises a prediction module to predict the possibility of progression fro
Externí odkaz: https://doaj.org/article/bc4942461d3d40a993c65a2412890fde
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4
Incidence and Survival Outcomes of Colorectal Cancer in Long-Term Metformin Users with Diabetes: A Population-Based Cohort Study Using a Common Data Model
Autor: Seung In Seo, Tae Jun Kim, Chan Hyuk Park, Chang Seok Bang, Kyung Joo Lee, Jinseob Kim, Hyon Hee Kim, Woon Geon Shin
Publikováno v: Journal of Personalized Medicine; Volume 12; Issue 4; Pages: 584
Background and aims: Previous studies have reported that metformin use in patients with diabetes mellitus may reduce the risk of colorectal cancer (CRC) incidence and prognosis; however, the evidence is not definite. This population-based cohort stud
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bc7d05e6720ec50f4196c0ec95855c44
https://pubmed.ncbi.nlm.nih.gov/35455700
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6
Mining Semantic Tags in a Content Analysis System for a Letter Database of Ethnic Koreans Living in China
Autor: Yuntae Kim, Jinnam Jo, Soungho Ye, Jungsun Choi, Joo-mi Lee, Hyong-Jin Moon, Seunghyun Seo, Hyon Hee Kim
Publikováno v: Advances in Computer Science and Ubiquitous Computing ISBN: 9789811393402
In this paper, we present a content analysis system for the letter database for ethnic Koreans living in China using several machine learning techniques. First, letters from ethnic Koreans living in China to Korea Broadcasting System (KBS) to find se
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::0830f76e98639ef652374795a782fe2a
https://doi.org/10.1007/978-981-13-9341-9_55
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7
Analyzing Twitter Data of Family Caregivers of Alzheimer’s Disease Patients Based on the Depression Ontology
Autor: Hyon Hee Kim, Donghee Shin, So-Hee Jeong, Annie Kim
Publikováno v: Advances in Computer Science and Ubiquitous Computing ISBN: 9789811393402
In this paper, we present an ontology-based approach to analyze depression of family caregivers of Alzheimer’s disease patients. First, we developed depression ontology called OntoDepression considering the language written in social media. Four ma
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::6bec2afa5ad9e16d1dcef023419be223
https://doi.org/10.1007/978-981-13-9341-9_6
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8
A Deep Recurrent Neural Network-Based Explainable Prediction Model for Progression from Atrophic Gastritis to Gastric Cancer
Autor: Kyung Joo Lee, Woon Geon Shin, Hyon Hee Kim, Young Seo Lim, Seung-In Seo, Jaeyoung Kim
Publikováno v: Applied Sciences; Volume 11; Issue 13; Pages: 6194
Applied Sciences, Vol 11, Iss 6194, p 6194 (2021)
Gastric cancer is the fifth most common cancer type worldwide and one of the most frequently diagnosed cancers in South Korea. In this study, we propose DeepPrevention, which comprises a prediction module to predict the possibility of progression fro
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a5d43291d267fe86096d9f53e48e0209
https://doi.org/10.3390/app11136194
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