Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Hyunah Shin"'
Publikováno v:
Digital Health, Vol 10 (2024)
With the advent of the big data era, data security issues are becoming more common. Healthcare organizations have more data to use for analysis, but they lose money every year due to their inability to prevent data leakage. To overcome these challeng
Externí odkaz:
https://doaj.org/article/82a71069a7764764a66e7ae1ea623e47
Autor:
Suncheol Heo, Eun-Ae Kang, Jae Yong Yu, Hae Reong Kim, Suehyun Lee, Kwangsoo Kim, Yul Hwangbo, Rae Woong Park, Hyunah Shin, Kyeongmin Ryu, Chungsoo Kim, Hyojung Jung, Yebin Chegal, Jae-Hyun Lee, Yu Rang Park
Publikováno v:
JMIR Medical Informatics, Vol 12, Pp e47693-e47693 (2024)
Abstract BackgroundAcute kidney injury (AKI) is a marker of clinical deterioration and renal toxicity. While there are many studies offering prediction models for the early detection of AKI, those predicting AKI occurrence using distributed research
Externí odkaz:
https://doaj.org/article/ea461c033d7041808a88999da72bd433
Autor:
Suncheol Heo, Jae Yong Yu, Eun Ae Kang, Hyunah Shin, Kyeongmin Ryu, Chungsoo Kim, Yebin Chegal, Hyojung Jung, Suehyun Lee, Rae Woong Park, Kwangsoo Kim, Yul Hwangbo, Jae-Hyun Lee, Yu Rang Park
Publikováno v:
Healthcare Informatics Research, Vol 29, Iss 3, Pp 246-255 (2023)
Objectives The objective of this study was to develop and validate a multicenter-based, multi-model, time-series deep learning model for predicting drug-induced liver injury (DILI) in patients taking angiotensin receptor blockers (ARBs). The study le
Externí odkaz:
https://doaj.org/article/3522e530e278447583fa81e8265322ea
Autor:
Suncheol Heo, Jae Yong Yu, Eun Ae Kang, Hyunah Shin, Kyeongmin Ryu, Chungsoo Kim, Yebin Chega, Hyojung Jung, Suehyun Lee, Rae Woong Park, Kwangsoo Kim, Yul Hwangbo, Jae-Hyun Lee, Yu Rang Park
Publikováno v:
Healthcare Informatics Research, Vol 30, Iss 2, Pp 168-168 (2024)
Externí odkaz:
https://doaj.org/article/8d50afee057c420d9d436c2879377639
Autor:
Suehyun Lee, Jeong Hoon Lee, Grace Juyun Kim, Jong-Yeup Kim, Hyunah Shin, Inseok Ko, Seon Choe, Ju Han Kim
Publikováno v:
Journal of Medical Internet Research, Vol 24, Iss 10, p e35464 (2022)
BackgroundPharmacovigilance using real-world data (RWD), such as multicenter electronic health records (EHRs), yields massively parallel adverse drug reaction (ADR) signals. However, proper validation of computationally detected ADR signals is not po
Externí odkaz:
https://doaj.org/article/e6772ad4406c44df96754ea0e1322006
Autor:
Hyunah Shin, Suehyun Lee
Publikováno v:
BMC Medical Informatics and Decision Making, Vol 21, Iss 1, Pp 1-11 (2021)
Abstract Background Adverse drug reactions (ADRs) are regarded as a major cause of death and a major contributor to public health costs. For the active surveillance of drug safety, the use of real-world data and real-world evidence as part of the ove
Externí odkaz:
https://doaj.org/article/47a51c41732b4da397f6c727a1c53acb
Autor:
Hyunah Shin, Jaehun Cha, Chungchun Lee, Hyejin Song, Hyuntae Jeong, Jong-Yeup Kim, Suehyun Lee
Publikováno v:
Applied Sciences, Vol 11, Iss 5, p 2249 (2021)
Pharmacovigilance, the scientific discipline pertaining to drug safety, has been studied extensively and is progressing continuously. In this field, medical informatics techniques and interpretation play important roles, and appropriate approaches ar
Externí odkaz:
https://doaj.org/article/19a90423b2a9496189110f88efca9e87
Autor:
Suncheol Heo, Eun-Ae Kang, Jae Yong Yu, Hae Reong Kim, Suehyun Lee, Kwangsoo Kim, Yul Hwangbo, Rae Woong Park, Hyunah Shin, Kyeongmin Ryu, Chungsoo Kim, Hyojung Jung, Yebin Chegal, Jae-Hyun Lee, Yu Rang Park
BACKGROUND Acute kidney injury (AKI) is a marker of clinical deterioration and renal toxicity. While there are many studies offering prediction models for the early detection of AKI, those predicting AKI occurrence using distributed research network
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0ded5e8fa75784da61d2a1c88d09e031
https://doi.org/10.2196/preprints.47693
https://doi.org/10.2196/preprints.47693
BACKGROUND Recent advances in smartphones and wearable devices have led to an increasing number of mHealth mobile applications (mHealth apps), enabling consumers to obtain drug information without the need for hospital visits. Consequently, interest
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3cb7512ebb5cf36e8d6d76c18bee1392
https://doi.org/10.2196/preprints.46238
https://doi.org/10.2196/preprints.46238
With the emergence of the 4th industrial revolution, demand for technologies that process and analyze big data in the healthcare has increased. As research is actively conducted, problems related to the protection of personal information included in
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::48815e45d0c0e815a30c34314a6af4f7
https://doi.org/10.21203/rs.3.rs-2035438/v1
https://doi.org/10.21203/rs.3.rs-2035438/v1