Jaehoon Oh

Model Agnostic Meta-Learning (MAML) is one of the most representative of gradient-based meta-learning algorithms. MAML learns new tasks with a few data samples using inner updates from a meta-initialization point and learns the meta-initialization parameters with outer updates. It has recently been hypothesized that representation reuse, which makes little change in efficient representations, is the dominant factor in the performance of the meta-initialized model through MAML in contrast to repr

Federated learning has evolved to improve a single global model under data heterogeneity (as a curse) or to develop multiple personalized models using data heterogeneity (as a blessing). However, little research has considered both directions simultaneously. In this paper, we first investigate the relationship between them by analyzing Federated Averaging at the client level and determine that a better federated global model performance does not constantly improve personalization. To elucidate t

Previous studies have demonstrated the potential for using smartwatches with a built-in accelerometer as feedback devices for high-quality chest compression during cardiopulmonary resuscitation. However, to the best of our knowledge, no previous study has reported the effects of this feedback on chest compressions in action. A randomized, parallel controlled study of 40 senior medical students was conducted to examine the effect of chest compression feedback via a smartwatch during cardiopulmona

<i>Objective</i>. There are many smartphone-based applications (apps) for cardiopulmonary resuscitation (CPR) training. We investigated the conformity and the learnability/usability of these apps for CPR training and real-life supports. <i>Methods</i>. We conducted a mixed-method, sequential explanatory study to assess CPR training apps downloaded on two apps stores in South Korea. Apps were collected with inclusion criteria as follows, Korean-language instruction, training features, and emergen

Continue CPR Attach monitoring/defibrillator Give 100% oxygen Shockable rhythm? VF/pVT Defibrillation CPR 2 min IV/IO access Epinephrine 1 mg every 3-5 min CPR 2 min IV/IO access Epinephrine 1 mg every 3-5 min Asystole

Acute thoracic aortic dissection is a life-threatening disease, in which blood leaking from the damaged inner layer of the aorta causes dissection between the intimal and adventitial layers. The diagnosis of this disease is challenging. Chest x-rays are usually performed for initial screening or diagnosis, but the diagnostic accuracy of this method is not high. Recently, deep learning has been successfully applied in multiple medical image analysis tasks. In this paper, we attempt to increase th

Feedback devices have been shown to improve the quality of chest compression during cardiopulmonary resuscitation for patients in the supine position, but no studies have reported the effects of feedback devices on chest compression when the chest is tilted. Basic life support-trained providers were randomly assigned to administer chest compressions to a manikin in the supine, 30° left lateral tilt and 30° semirecumbent positions, with or without the aid of a feedback device incorporated into a

<b>Background:</b> The coronavirus disease 2019 (COVID-19) pandemic has caused deaths and shortages in medical resources worldwide, making the prediction of patient prognosis and the identification of risk factors very important. Increasing age is already known as one of the main risk factors for poor outcomes, but the effect of body mass index (BMI) on COVID-19 outcomes in older patients has not yet been investigated. <b>Aim:</b> We aimed to determine the effect of BMI on the severity and morta

Increased body mass index (BMI) is a risk factor for cardiovascular disease, stroke, and metabolic diseases. A high BMI may affect outcomes of post-cardiac arrest patients, but the association remains debatable. We aimed to determine the association between BMI and outcomes in patients with return of spontaneous circulation (ROSC) after out-of-hospital cardiac arrest (OHCA). A systematic literature search was conducted using MEDLINE, EMBASE, and the Cochrane Library. Studies that included patien

Cross-domain few-shot learning (CD-FSL), where there are few target samples under extreme differences between source and target domains, has recently attracted huge attention. Recent studies on CD-FSL generally focus on transfer learning based approaches, where a neural network is pre-trained on popular labeled source domain datasets and then transferred to target domain data. Although the labeled datasets may provide suitable initial parameters for the target data, the domain difference between