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| 1 | Abnormal liver chemistries as a predictor of COVID-19 severity and clinical outcomes in hospitalized patients显示文摘BACKGROUND Abnormal liver chemistries are common findings in patients with Coronavirus Disease 2019(COVID-19).However,the association of these abnormalities with the severity of COVID-19 and clinical outcomes is poorly understood AIM We aimed to assess the prevalence of elevated liver chemistries in hospitalized patients with COVID-19 and compare the serum liver chemistries to predict the severity and in-hospital mortality.METHODS This retrospective,observational study included 3380 patients with COVID-19 who were hospitalized in the Johns Hopkins Health System(Baltimore,MD,United States).Demographic data,clinical characteristics,laboratory findings,treatment measures,and outcome data were collected.Cox regression modeling was used to explore variables associated with abnormal liver chemistries on admission with disease severity and prognosis RESULTS A total of 2698(70.4%)had abnormal alanine aminotransferase(ALT)at the time of admission.Other more prevalent abnormal liver chemistries were aspartate aminotransferase(AST)(44.4%),alkaline phosphatase(ALP)(16.1%),and total bilirubin(T-Bil)(5.9%).Factors associated with liver injury were older age,Asian ethnicity,other race,being overweight,and obesity.Higher ALT,AST,T-Bil,and ALP levels were more commonly associated with disease severity.Multivariable adjusted Cox regression analysis revealed that abnormal AST and T-Bil were associated with the highest mortality risk than other liver injury indicators during hospitalization.Abnormal AST,T-Bil,and ALP were associated with a need for vasopressor drugs,whereas higher levels of AST,T-Bil,and a decreased albumin levels were associated with mechanical ventilation CONCLUSION Abnormal liver chemistries are common at the time of hospital admission in COVID-19 patients and can be closely related to the patient’s severity and prognosis.Elevated liver chemistries,specifically ALT,AST,ALP,and T-Bil levels,can be used to stratify risk and predict the need for advanced therapies in these patients. | Arunkumar Krishnan Laura Prichett Xueting Tao Saleh A Alqahtani James P Hamilton Esteban Mezey Alexandra T Strauss Ahyoung Kim James J Potter Po-Hung Chen Tinsay A Woreta | 2022 | World Journal of Gastroenterology2022，28，5： | 2 |
| 2 | Mechanoelectrical remodeling and arrhythmias during progression of hypertrophy 显示文摘 | Hongwei Jin ChemalyElie R Ahyoung Lee | 2010 | FASEB J2010，24，2： | 1 |
| 3 | Cardiac CT Image Segmentation for Deep Learning-Based Coronary Calcium Detection Using K-Means Clustering and Grabcut Algorithm显示文摘Specific medical data has limitations in that there are not many numbers and it is not standardized.to solve these limitations,it is necessary to study how to efficiently process these limited amounts of data.In this paper,deep learning methods for automatically determining cardiovascular diseases are described,and an effective preprocessing method for CT images that can be applied to improve the performance of deep learning was conducted.The cardiac CT images include several parts of the body such as the heart,lungs,spine,and ribs.The preprocessing step proposed in this paper divided CT image data into regions of interest and other regions using K-means clustering and the Grabcut algorithm.We compared the deep learning performance results of original data,data using only K-means clustering,and data using both K-means clustering and the Grabcut algorithm.All data used in this paper were collected at Soonchunhyang University Cheonan Hospital in Korea and the experimental test proceeded with IRB approval.The training was conducted using Resnet 50,VGG,and Inception resnet V2 models,and Resnet 50 had the best accuracy in validation and testing.Through the preprocessing process proposed in this paper,the accuracy of deep learning models was significantly improved by at least 10%and up to 40%. | Sungjin Lee Ahyoung Lee Min Hong | 2023 | Computer Systems Science & Engineering2023，46，8： | 1 |
| 4 | An Interface Test Model for Hardwaredependent Software and Embedded OS API of the Embedded System显示文摘 | Ahyoung S Byoungju C | 2007 | Computer Standards & Interfaces2007，29，4： | 1 |
| 5 | Pharmacokinetic Comparison of Sustained- and Immediate-Release Oral Formulations of Cilostazol in Healthy Korean Subjects: A Randomized, Open-Label, 3-Part, Sequential, 2-Period, Crossover, Single-Dose, Food-Effect, and Multiple-Dose Study显示文摘 | Donghwan Lee Lay Ahyoung Lim Seong Bok Jang Yoon Jung Lee Jae Yong Chung Jong Rak Choi Kiyoon Kim Jin Woo Park Hosang Yoon Jaeyong Lee Min Soo Park Kyungsoo Park | 2011 | Clinical Therapeutics2011，，12： | 1 |
| 6 | An interface test model for hardware-dependent software and embedded OS API of the embedded system显示文摘 | Ahyoung S Byoungju C | 2007 | Computer Standards & Interfaces2007，29，4： | 1 |
| 7 | Altered sarcoplasmic reticulum calcium cycling-targets for heart failure therapy显示文摘 | Changwon Kho Ahyoung Lee Roger J | 2012 | Nat Rev Cardiol2012，9，12： | 1 |
| 8 | An Interface Test Model for Hardware-dependent Software and Embedded OS API of the Embeded System显示文摘 | Ahyoung Sung Byoungju Choi Seokkyoo Shin | 2007 | Computer Standard & Interfaces2007，29，： | 1 |
| 9 | Some remarks on chain prolongations in dynamical systems 显示文摘 | YUN C H AHYOUNG K SUH P J | 1999 | Journag of ~he Chungcheong Mathematical Society1999，32，： | 1 |
| 10 | Effect of HMGCR Variant Alleles on Low-Density Lipoprotein Cholesterol-Lowering Response to Atorvastatin in Healthy Korean Subjects显示文摘 | Jae Yong Chung Sung Kweon Cho Eun Sil Oh Dong Hwan Lee Lay Ahyoung Lim Seong Bok Jang Yoon Jung Lee Kyungsoo Park Min Soo Park | 2012 | Journal of Clinical Pharmacology2012，，3： | 1 |
| 11 | Multi-Agent Deep Q-Networks for Efficient Edge Federated Learning Communications in Software-Defined IoT显示文摘Federated learning(FL)activates distributed on-device computation techniques to model a better algorithm performance with the interaction of local model updates and global model distributions in aggregation averaging processes.However,in large-scale heterogeneous Internet of Things(IoT)cellular networks,massive multi-dimensional model update iterations and resource-constrained computation are challenging aspects to be tackled significantly.This paper introduces the system model of converging softwaredefined networking(SDN)and network functions virtualization(NFV)to enable device/resource abstractions and provide NFV-enabled edge FL(eFL)aggregation servers for advancing automation and controllability.Multi-agent deep Q-networks(MADQNs)target to enforce a self-learning softwarization,optimize resource allocation policies,and advocate computation offloading decisions.With gathered network conditions and resource states,the proposed agent aims to explore various actions for estimating expected longterm rewards in a particular state observation.In exploration phase,optimal actions for joint resource allocation and offloading decisions in different possible states are obtained by maximum Q-value selections.Action-based virtual network functions(VNF)forwarding graph(VNFFG)is orchestrated to map VNFs towards eFL aggregation server with sufficient communication and computation resources in NFV infrastructure(NFVI).The proposed scheme indicates deficient allocation actions,modifies the VNF backup instances,and reallocates the virtual resource for exploitation phase.Deep neural network(DNN)is used as a value function approximator,and epsilongreedy algorithm balances exploration and exploitation.The scheme primarily considers the criticalities of FL model services and congestion states to optimize long-term policy.Simulation results presented the outperformance of the proposed scheme over reference schemes in terms of Quality of Service(QoS)performance metrics,including packet drop ratio,packet drop counts,packet delivery ratio,delay,and throughput. | Prohim Tam Sa Math Ahyoung Lee Seokhoon Kim | 2022 | Computers, Materials & Continua2022，，5： | 0 |
| 12 | Hybrid Mobile Cloud Computing Architecture with Load Balancing for Healthcare Systems显示文摘Healthcare is a fundamental part of every individual’s life.The healthcare industry is developing very rapidly with the help of advanced technologies.Many researchers are trying to build cloud-based healthcare applications that can be accessed by healthcare professionals from their premises,as well as by patients from their mobile devices through communication interfaces.These systems promote reliable and remote interactions between patients and healthcare professionals.However,there are several limitations to these innovative cloud computing-based systems,namely network availability,latency,battery life and resource availability.We propose a hybrid mobile cloud computing(HMCC)architecture to address these challenges.Furthermore,we also evaluate the performance of heuristic and dynamic machine learning based task scheduling and load balancing algorithms on our proposed architecture.We compare them,to identify the strengths and weaknesses of each algorithm;and provide their comparative results,to show latency and energy consumption performance.Challenging issues for cloudbased healthcare systems are discussed in detail. | Ahyoung Lee Jui Mhatre Rupak Kumar Das Min Hong | 2023 | Computers, Materials & Continua2023，，1： | 0 |
| 13 | LIV-4:A novel model for predicting transplant-free survival in critically ill cirrhotics显示文摘BACKGROUND Critically ill patients with cirrhosis,particularly those with acute decompensation,have higher mortality rates in the intensive care unit(ICU)than patients without chronic liver disease.Prognostication of short-term mortality is important in order to identify patients at highest risk of death.None of the currently available prognostic models have been widely accepted for use in cirrhotic patients in the ICU,perhaps due to complexity of calculation,or lack of universal variables readily available for these patients.We believe a survival model meeting these requirements can be developed,to guide therapeutic decision-making and contribute to cost-effective healthcare resource utilization.AIM To identify markers that best identify likelihood of survival and to determine the performance of existing survival models.METHODS Consecutive cirrhotic patients admitted to a United States quaternary care center ICU between 2008-2014 were included and comprised the training cohort.Demographic data and clinical laboratory test collected on admission to ICU were analyzed.Area under the curve receiver operator characteristics(AUROC)analysis was performed to assess the value of various scores in predicting inhospital mortality.A new predictive model,the LIV-4 score,was developed using logistic regression analysis and validated in a cohort of patients admitted to the same institution between 2015-2017.RESULTS Of 436 patients,119(27.3%)died in the hospital.In multivariate analysis,a combination of the natural logarithm of the bilirubin,prothrombin time,white blood cell count,and mean arterial pressure was found to most accurately predict in-hospital mortality.Derived from the regression coefficients of the independent variables,a novel model to predict inpatient mortality was developed(the LIV-4 score)and performed with an AUROC of 0.86,compared to the Model for End-Stage Liver Disease,Chronic Liver Failure-Sequential Organ Failure Assessment,and Royal Free Hospital Score,which performed with AUROCs of 0.81,0.80,and 0.77,respectively.Patients in the internal validation cohort were substantially sicker,as evidenced by higher Model for End-Stage Liver Disease,Model for End-Stage Liver Disease-Sodium,Acute Physiology and Chronic Health Evaluation III,SOFA and LIV-4 scores.Despite these differences,the LIV-4 score remained significantly higher in subjects who expired during the hospital stay and exhibited good prognostic values in the validation cohort with an AUROC of 0.80.CONCLUSION LIV-4,a validated model for predicting mortality in cirrhotic patients on admission to the ICU,performs better than alternative liver and ICU-specific survival scores. | Christina C Lindenmeyer Gianina Flocco Vedha Sanghi Rocio Lopez Ahyoung J Kim Fadi Niyazi Neal A Mehta Aanchal Kapoor William D Carey Eduardo Mireles-Cabodevila Carlos Romero-Marrero | 2020 | World Journal of Hepatology2020，12，6： | 0 |
| 14 | Mood (affective) disorders显示文摘Mood disorders (including unipolar and bipolar disorders)are common. They are characterized by persistent states of abnormal mood, which may be elevated, depressed or both alternately. | lainMachmillan AHYoung INicolFerrier | 2004 | 国际内科双语杂志(中英文)2004，4，12： | 0 |
| 15 | 心境(情感)障碍显示文摘现认为双相障碍,特别是双相Ⅱ障碍(轻躁狂/抑郁,常被误诊为单相抑郁)比过去想像的多得多。 | IainMacmillan AHYoung INicolFerrier 武力勇 李舜伟 | 2004 | 国际内科双语杂志(中英文)2004，4，12： | 0 |
| 16 | An unsupervised anomaly detection framework for detecting anomalies in real time through network system’s log files analysis显示文摘Nowadays,in almost every computer system,log files are used to keep records of occurring events.Those log files are then used for analyzing and debugging system failures.Due to this important utility,researchers have worked on finding fast and efficient ways to detect anomalies in a computer system by analyzing its log records.Research in log-based anomaly detection can be divided into two main categories:batch log-based anomaly detection and streaming log-based anomaly detection.Batch log-based anomaly detection is computationally heavy and does not allow us to instantaneously detect anomalies.On the other hand,streaming anomaly detection allows for immediate alert.However,current streaming approaches are mainly supervised.In this work,we propose a fully unsupervised framework which can detect anomalies in real time.We test our framework on hdfs log files and successfully detect anomalies with an F-1 score of 83%. | Vannel Zeufack Donghyun Kim Daehee Seo Ahyoung Lee | 2021 | High-Confidence Computing2021，1，2： | 0 |
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