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RISS 인기검색어
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- 저자 : Lee Grace Yoojin (검색결과 3 건)
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Lee, Jimin,Noh, Yoojin,Shin, Sooyoung,Lim, Hong-Seok,Park, Rae Woong,Bae, Soo Kyung,Oh, Euichaul,Kim, Grace Juyun,Kim, Ju Han,Lee, Sukhyang Dove Medical Press 2016 THERAPEUTICS AND CLINICAL RISK MANAGEMENT Vol.12 No.-
<P>Statin therapy is beneficial in reducing cardiovascular events and mortalities in patients with atherosclerotic cardiovascular diseases. Yet, there have been concerns of increased risk of diabetes with statin use. This study was aimed to evaluate the association between statins and new onset diabetes mellitus (NODM) in patients with ischemic heart disease (IHD) utilizing the Korean Health Insurance Review and Assessment Service claims database. Among adult patients with preexisting IHD, new statin users and matched nonstatin users were identified on a 1:1 ratio using proportionate stratified random sampling by sex and age. They were subsequently propensity score matched further with age and comorbidities to reduce the selection bias. Overall incidence rates, cumulative rates and hazard ratios (HRs) between statin use and occurrence of NODM were estimated. The subgroup analyses were performed according to sex, age groups, and the individual agents and intensities of statins. A total of 156,360 patients (94,370 in the statin users and 61,990 in the nonstatin users) were included in the analysis. The incidence rates of NODM were 7.8% and 4.8% in the statin users and nonstatin users, respectively. The risk of NODM was higher among statin users (crude HR 2.01, 95% confidence interval [CI] 1.93–2.10; adjusted HR 1.84, 95% CI 1.63–2.09). Pravastatin had the lowest risk (adjusted HR 1.54, 95% CI 1.32–1.81) while those who were exposed to more than one statin were at the highest risk of NODM (adjusted HR 2.17, 95% CI 1.93–2.37). It has been concluded that all statins are associated with the risk of NODM in patients with IHD, and it is believed that our study would contribute to a better understanding of statin and NODM association by analyzing statin use in the real-world setting. Periodic screening and monitoring for diabetes are warranted during prolonged statin therapy in patients with IHD.</P>
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Status epilepticus due to cerebral air embolism after the Valsalva maneuver
유현지,Hye-Jeong Lee,Grace Yoojin Lee,김원주 대한신경집중치료학회 2019 대한신경집중치료학회지 Vol.12 No.1
Background: Cerebral air embolism is uncommon but potentially causes catastrophic events such as cardiac damage or even death. However, due to a low overall incidence, it may go undiagnosed. Case Report: A 56-year-old man with a medical history of right upper lobectomy due to lung cancer showed changes in mental status after the Valsalva maneuver, followed by status epilepticus during admission. Brain and chest computed tomography showed cerebral air embolism and accidental pneumothorax in the right major fissure. After antiepileptic drug infusion and oxygen therapy, he recovered completely. Conclusion: Since cerebral air embolism may result in fatal outcomes, it should be suspected in patients with sudden neurological deterioration after routine medical procedures.
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Hong Gil-Sun,Jang Miso,Kyung Sunggu,Cho Kyungjin,Jeong Jiheon,Lee Grace Yoojin,Shin Keewon,Kim Ki Duk,Ryu Seung Min,Seo Joon Beom,Lee Sang Min,Kim Namkug 대한영상의학회 2023 Korean Journal of Radiology Vol.24 No.11
Artificial intelligence (AI) in radiology is a rapidly developing field with several prospective clinical studies demonstrating its benefits in clinical practice. In 2022, the Korean Society of Radiology held a forum to discuss the challenges and drawbacks in AI development and implementation. Various barriers hinder the successful application and widespread adoption of AI in radiology, such as limited annotated data, data privacy and security, data heterogeneity, imbalanced data, model interpretability, overfitting, and integration with clinical workflows. In this review, some of the various possible solutions to these challenges are presented and discussed; these include training with longitudinal and multimodal datasets, dense training with multitask learning and multimodal learning, self-supervised contrastive learning, various image modifications and syntheses using generative models, explainable AI, causal learning, federated learning with large data models, and digital twins.
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