Yonsei University

About: Yonsei University is a education organization based out in Seoul, South Korea. It is known for research contribution in the topics: Population & Cancer. The organization has 50162 authors who have published 106172 publications receiving 2279044 citations. The organization is also known as: Yonsei.

Technology of deep brain stimulation: current status and future directions.

Abstract: Deep brain stimulation (DBS) is a neurosurgical procedure that allows targeted circuit-based neuromodulation. DBS is a standard of care in Parkinson disease, essential tremor and dystonia, and is also under active investigation for other conditions linked to pathological circuitry, including major depressive disorder and Alzheimer disease. Modern DBS systems, borrowed from the cardiac field, consist of an intracranial electrode, an extension wire and a pulse generator, and have evolved slowly over the past two decades. Advances in engineering and imaging along with an improved understanding of brain disorders are poised to reshape how DBS is viewed and delivered to patients. Breakthroughs in electrode and battery designs, stimulation paradigms, closed-loop and on-demand stimulation, and sensing technologies are expected to enhance the efficacy and tolerability of DBS. In this Review, we provide a comprehensive overview of the technical development of DBS, from its origins to its future. Understanding the evolution of DBS technology helps put the currently available systems in perspective and allows us to predict the next major technological advances and hurdles in the field.

Machine Learning-Based Model for Prediction of Outcomes in Acute Stroke.

Abstract: Background and Purpose- The prediction of long-term outcomes in ischemic stroke patients may be useful in treatment decisions. Machine learning techniques are being increasingly adapted for use in the medical field because of their high accuracy. This study investigated the applicability of machine learning techniques to predict long-term outcomes in ischemic stroke patients. Methods- This was a retrospective study using a prospective cohort that enrolled patients with acute ischemic stroke. Favorable outcome was defined as modified Rankin Scale score 0, 1, or 2 at 3 months. We developed 3 machine learning models (deep neural network, random forest, and logistic regression) and compared their predictability. To evaluate the accuracy of the machine learning models, we also compared them to the Acute Stroke Registry and Analysis of Lausanne (ASTRAL) score. Results- A total of 2604 patients were included in this study, and 2043 (78%) of them had favorable outcomes. The area under the curve for the deep neural network model was significantly higher than that of the ASTRAL score (0.888 versus 0.839; P<0.001), while the areas under the curves of the random forest (0.857; P=0.136) and logistic regression (0.849; P=0.413) models were not significantly higher than that of the ASTRAL score. Using only the 6 variables that are used for the ASTRAL score, the performance of the machine learning models did not significantly differ from that of the ASTRAL score. Conclusions- Machine learning algorithms, particularly the deep neural network, can improve the prediction of long-term outcomes in ischemic stroke patients.

Robot-assisted gastrectomy with lymph node dissection for gastric cancer: lessons learned from an initial 100 consecutive procedures

Abstract: Objective:To evaluate the technical feasibility, effectiveness, and safety of robot-assisted gastrectomy (RAG) with lymphadenectomy, using the da Vinci system through analyses of our initial series of 100 consecutive patients.Summary Background Data:The application of robotic surgery was proven to b

2014 clinical practice guidelines for overweight and obesity in Korea.

Abstract: The dramatic increase in the prevalence of obesity and its accompanying comorbidities are major health concerns in Korea. Obesity is defined as a body mass index ≥25 kg/m2 in Korea. Current estimates are that 32.8% of adults are obese: 36.1% of men and 29.7% of women. The prevalence of being overweight and obese in national surveys is increasing steadily. Early detection and the proper management of obesity are urgently needed. Weight loss of 5% to 10% is the standard goal. In obese patients, control of cardiovascular risk factors deserves the same emphasis as weight-loss therapy. Since obesity is multifactorial, proper care of obesity requires a coordinated multidisciplinary treatment team, as a single intervention is unlikely to modify the incidence or natural history of obesity.