Search Results - "Kwon, Joon‐myoung"

An Algorithm Based on Deep Learning for Predicting In-Hospital Cardiac Arrest by Kwon, Joon-Myoung, Lee, Youngnam, Lee, Yeha, Lee, Seungwoo, Park, Jinsik

“…In-hospital cardiac arrest is a major burden to public health, which affects patient safety. Although traditional track-and-trigger systems are used to predict…”
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Deep Learning-Based Algorithm for Detecting Aortic Stenosis Using Electrocardiography by Kwon, Joon-Myoung, Lee, Soo Youn, Jeon, Ki-Hyun, Lee, Yeha, Kim, Kyung-Hee, Park, Jinsik, Oh, Byung-Hee, Lee, Myong-Mook

“…Background Severe, symptomatic aortic stenosis (AS) is associated with poor prognoses. However, early detection of AS is difficult because of the long…”
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Artificial intelligence algorithm for detecting myocardial infarction using six-lead electrocardiography by Cho, Younghoon, Kwon, Joon-myoung, Kim, Kyung-Hee, Medina-Inojosa, Jose R., Jeon, Ki-Hyun, Cho, Soohyun, Lee, Soo Youn, Park, Jinsik, Oh, Byung-Hee

“…Rapid diagnosis of myocardial infarction (MI) using electrocardiography (ECG) is the cornerstone of effective treatment and prevention of mortality; however,…”
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Deep learning for predicting in‐hospital mortality among heart disease patients based on echocardiography by Kwon, Joon‐myoung, Kim, Kyung‐Hee, Jeon, Ki‐Hyun, Park, Jinsik

“…Background Heart disease (HD) is the leading cause of global death; there are several mortality prediction models of HD for identifying critically‐ill patients…”
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Validation of deep-learning-based triage and acuity score using a large national dataset by Kwon, Joon-Myoung, Lee, Youngnam, Lee, Yeha, Lee, Seungwoo, Park, Hyunho, Park, Jinsik

“…Triage is important in identifying high-risk patients amongst many less urgent patients as emergency department (ED) overcrowding has become a national crisis…”
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Artificial intelligence for early prediction of pulmonary hypertension using electrocardiography by Kwon, Joon-myoung, Kim, Kyung-Hee, Medina-Inojosa, Jose, Jeon, Ki-Hyun, Park, Jinsik, Oh, Byung-Hee

“…Screening and early diagnosis of pulmonary hypertension (PH) are critical for managing progression and preventing associated mortality; however, there are no…”
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Comparing the performance of artificial intelligence and conventional diagnosis criteria for detecting left ventricular hypertrophy using electrocardiography by Kwon, Joon-Myoung, Jeon, Ki-Hyun, Kim, Hyue Mee, Kim, Min Jeong, Lim, Sung Min, Kim, Kyung-Hee, Song, Pil Sang, Park, Jinsik, Choi, Rak Kyeong, Oh, Byung-Hee

“…Abstract Aims  Although left ventricular hypertrophy (LVH) has a high incidence and clinical importance, the conventional diagnosis criteria for detecting LVH…”
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Predicting intraoperative hypotension using deep learning with waveforms of arterial blood pressure, electroencephalogram, and electrocardiogram: Retrospective study by Jo, Yong-Yeon, Jang, Jong-Hwan, Kwon, Joon-myoung, Lee, Hyung-Chul, Jung, Chul-Woo, Byun, Seonjeong, Jeong, Han-Gil

“…To develop deep learning models for predicting Interoperative hypotension (IOH) using waveforms from arterial blood pressure (ABP), electrocardiogram (ECG),…”
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Deep-learning-based risk stratification for mortality of patients with acute myocardial infarction by Kwon, Joon-Myoung, Jeon, Ki-Hyun, Kim, Hyue Mee, Kim, Min Jeong, Lim, Sungmin, Kim, Kyung-Hee, Song, Pil Sang, Park, Jinsik, Choi, Rak Kyeong, Oh, Byung-Hee

“…Conventional risk stratification models for mortality of acute myocardial infarction (AMI) have potential limitations. This study aimed to develop and validate…”
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Artificial intelligence algorithm for predicting mortality of patients with acute heart failure by Kwon, Joon-Myoung, Kim, Kyung-Hee, Jeon, Ki-Hyun, Lee, Sang Eun, Lee, Hae-Young, Cho, Hyun-Jai, Choi, Jin Oh, Jeon, Eun-Seok, Kim, Min-Seok, Kim, Jae-Joong, Hwang, Kyung-Kuk, Chae, Shung Chull, Baek, Sang Hong, Kang, Seok-Min, Choi, Dong-Ju, Yoo, Byung-Su, Kim, Kye Hun, Park, Hyun-Young, Cho, Myeong-Chan, Oh, Byung-Hee

“…This study aimed to develop and validate deep-learning-based artificial intelligence algorithm for predicting mortality of AHF (DAHF). 12,654 dataset from 2165…”
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Deep-learning-based out-of-hospital cardiac arrest prognostic system to predict clinical outcomes by Kwon, Joon-myoung, Jeon, Ki-Hyun, Kim, Hyue Mee, Kim, Min Jeong, Lim, Sungmin, Kim, Kyung-Hee, Song, Pil Sang, Park, Jinsik, Choi, Rak Kyeong, Oh, Byung-Hee

“…Out-of-hospital cardiac arrest (OHCA) is a major healthcare burden, and prognosis is critical in decision-making for treatment and the withdrawal of…”
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Artificial intelligence algorithm to predict the need for critical care in prehospital emergency medical services by Kang, Da-Young, Cho, Kyung-Jae, Kwon, Oyeon, Kwon, Joon-Myoung, Jeon, Ki-Hyun, Park, Hyunho, Lee, Yeha, Park, Jinsik, Oh, Byung-Hee

“…In emergency medical services (EMSs), accurately predicting the severity of a patient's medical condition is important for the early identification of those…”
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Artificial intelligence for detecting mitral regurgitation using electrocardiography by Kwon, Joon-myoung, Kim, Kyung-Hee, Akkus, Zeynettin, Jeon, Ki-Hyun, Park, Jinsik, Oh, Byung-Hee

“…Screening and early diagnosis of mitral regurgitation (MR) are crucial for preventing irreversible progression of MR. In this study, we developed and validated…”
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Artificial intelligence for detecting electrolyte imbalance using electrocardiography by Kwon, Joon‐myoung, Jung, Min‐Seung, Kim, Kyung‐Hee, Jo, Yong‐Yeon, Shin, Jae‐Hyun, Cho, Yong‐Hyeon, Lee, Yoon‐Ji, Ban, Jang‐Hyeon, Jeon, Ki‐Hyun, Lee, Soo Youn, Park, Jinsik, Oh, Byung‐Hee

“…Introduction The detection and monitoring of electrolyte imbalance is essential for appropriate management of many metabolic diseases; however, there is no…”
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Development and Validation of Deep-Learning Algorithm for Electrocardiography-Based Heart Failure Identification by Kwon, Joon Myoung, Kim, Kyung Hee, Jeon, Ki Hyun, Kim, Hyue Mee, Kim, Min Jeong, Lim, Sung Min, Song, Pil Sang, Park, Jinsik, Choi, Rak Kyeong, Oh, Byung Hee

“…Screening and early diagnosis for heart failure (HF) are critical. However, conventional screening diagnostic methods have limitations, and electrocardiography…”
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Artificial intelligence algorithm for predicting cardiac arrest using electrocardiography by Kwon, Joon-Myoung, Kim, Kyung-Hee, Jeon, Ki-Hyun, Lee, Soo Youn, Park, Jinsik, Oh, Byung-Hee

“…In-hospital cardiac arrest is a major burden in health care. Although several track-and-trigger systems are used to predict cardiac arrest, they often have…”
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Development of an Interoperable and Easily Transferable Clinical Decision Support System Deployment Platform: System Design and Development Study by Yoo, Junsang, Lee, Jeonghoon, Min, Ji Young, Choi, Sae Won, Kwon, Joon-Myoung, Cho, Insook, Lim, Chiyeon, Choi, Mi Young, Cha, Won Chul

“…A clinical decision support system (CDSS) is recognized as a technology that enhances clinical efficacy and safety. However, its full potential has not been…”
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Applicable Machine Learning Model for Predicting Contrast-induced Nephropathy Based on Pre-catheterization Variables by Choi, Heejung, Choi, Byungjin, Han, Sungdam, Lee, Minjeong, Shin, Gyu-Tae, Kim, Heungsoo, Son, Minkook, Kim, Kyung-Hee, Kwon, Joon-myoung, Park, Rae Woong, Park, Inwhee

“…Objective Contrast agents used for radiological examinations are an important cause of acute kidney injury (AKI). We developed and validated a machine learning…”
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ROMIAE (Rule-Out Acute Myocardial Infarction Using Artificial Intelligence Electrocardiogram Analysis) trial study protocol: a prospective multicenter observational study for validation of a deep learning-based 12-lead electrocardiogram analysis model for detecting acute myocardial infarction in patients visiting the emergency department by Shin, Tae Gun, Lee, Youngjoo, Kim, Kyuseok, Lee, Min Sung, Kwon, Joon-Myoung

“…Based on the development of artificial intelligence (AI), an emerging number of methods have achieved outstanding performances in the diagnosis of acute…”
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Quick Sequential Organ Failure Assessment Score and the Modified Early Warning Score for Predicting Clinical Deterioration in General Ward Patients Regardless of Suspected Infection by Ko, Ryoung-Eun, Kwon, Oyeon, Cho, Kyung-Jae, Lee, Yeon Joo, Kwon, Joon-Myoung, Park, Jinsik, Kim, Jung Soo, Kim, Ah Jin, Jo, You Hwan, Lee, Yeha, Jeon, Kyeongman

“…The quick sequential organ failure assessment (qSOFA) score is suggested to use for screening patients with a high risk of clinical deterioration in the…”
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