AIMC Topic: Republic of Korea

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Comparing Logistic Regression Models with Alternative Machine Learning Methods to Predict the Risk of Drug Intoxication Mortality.

International journal of environmental research and public health Jan 31, 2020
(1) Medical research has shown an increasing interest in machine learning, permitting massive multivariate data analysis. Thus, we developed drug intoxication mortality prediction models, and compared machine learning models and traditional logistic ...
Republic of Korea Risk Assessment Logistic Models Area Under Curve Neural Networks, Computer Machine Learning Mortality Alcoholic Intoxication
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Economic Evaluation of Robot-Based Telemedicine Consultation Services.

Telemedicine journal and e-health : the official journal of the American Telemedicine Association Jan 22, 2020
Through information and communication technology, telemedicine can deliver medical care without time and place restrictions, increasing health care accessibility in medically underdeveloped regions. Although there is growing interest in using robots...
Robotics Humans Telemedicine Republic of Korea Cost-Benefit Analysis Referral and Consultation
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Comparative clinical evaluation of atlas and deep-learning-based auto-segmentation of organ structures in liver cancer.

Radiation oncology (London, England) Nov 27, 2019
BACKGROUND: Accurate and standardized descriptions of organs at risk (OARs) are essential in radiation therapy for treatment planning and evaluation. Traditionally, physicians have contoured patient images manually, which, is time-consuming and subje...
Radiotherapy Planning, Computer-Assisted Observer Variation Radiotherapy Deep Learning Liver Neoplasms Image Processing, Computer-Assisted Organs at Risk Neural Networks, Computer Tomography, X-Ray Computed Republic of Korea Female Reproducibility of Results Aged Male Middle Aged Humans
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The exploration of feature extraction and machine learning for predicting bone density from simple spine X-ray images in a Korean population.

Skeletal radiology Nov 23, 2019
OBJECTIVE: Osteoporosis is hard to detect before it manifests symptoms and complications. In this study, we evaluated machine learning models for identifying individuals with abnormal bone mineral density (BMD) through an analysis of spine X-ray feat...
Humans Republic of Korea Female Sensitivity and Specificity Male Machine Learning Bone Density Retrospective Studies Osteoporosis Radiographic Image Interpretation, Computer-Assisted Middle Aged Spine Predictive Value of Tests Absorptiometry, Photon Radiography
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Prediction of marbling score and carcass traits in Korean Hanwoo beef cattle using machine learning methods and synthetic minority oversampling technique.

Meat science Nov 12, 2019
Pricing of Hanwoo beef in the Korean market is primarily based on meat quality, and particularly on marbling score. The ability to accurately predict marbling score early in the life of an animal is extremely valuable for producers to meet the requir...
Image Processing, Computer-Assisted Red Meat Food Quality Animals Machine Learning Ultrasonography Republic of Korea Cattle Reproducibility of Results
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Predicting the Swallow-Related Quality of Life of the Elderly Living in a Local Community Using Support Vector Machine.

International journal of environmental research and public health Nov 3, 2019
This study developed a support vector machine (SVM) algorithm-based prediction model with considering influence factors associated with the swallowing quality-of-life as the predictor variables and provided baseline information for enhancing the swa...
Deglutition Disorders Deglutition Republic of Korea Female Aged Aged, 80 and over Cross-Sectional Studies Male Quality of Life Support Vector Machine Humans
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Machine learning-based prediction of radiographic progression in patients with axial spondyloarthritis.

Clinical rheumatology Oct 30, 2019
INTRODUCTION: Machine learning is applied to characterize the risk and predict outcomes in multi-dimensional data. The prediction of radiographic progression in axial spondyloarthritis (axSpA) remains limited. Hence, we tested the feasibility of supe...
Spondylarthritis Severity of Illness Index Radiography Spine Humans Adult Male Machine Learning Middle Aged Retrospective Studies Disease Progression Republic of Korea Female
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Atrial fibrillation classification based on convolutional neural networks.

BMC medical informatics and decision making Oct 29, 2019
BACKGROUND: The global age-adjusted mortality rate related to atrial fibrillation (AF) registered a rapid growth in the last four decades, i.e., from 0.8 to 1.6 and 0.9 to 1.7 per 100,000 for men and women during 1990-2010, respectively. In this cont...
Female Male Disease Progression Hospitals Diagnosis, Computer-Assisted Electrocardiography Atrial Fibrillation Glycosphingolipids Humans Neural Networks, Computer Republic of Korea
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Machine learning for prediction of septic shock at initial triage in emergency department.

Journal of critical care Oct 22, 2019
BACKGROUND: We hypothesized utilizing machine learning (ML) algorithms for screening septic shock in ED would provide better accuracy than qSOFA or MEWS.
Emergency Service, Hospital Female Shock, Septic Multivariate Analysis Humans Aged Algorithms Male Vital Signs Machine Learning Young Adult Support Vector Machine Middle Aged Adult Sepsis Hospital Mortality Software Prognosis Electronic Health Records Republic of Korea Triage Area Under Curve
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Depression Prediction by Using Ecological Momentary Assessment, Actiwatch Data, and Machine Learning: Observational Study on Older Adults Living Alone.

JMIR mHealth and uHealth Oct 16, 2019
BACKGROUND: Although geriatric depression is prevalent, diagnosis using self-reporting instruments has limitations when measuring the depressed mood of older adults in a community setting. Ecological momentary assessment (EMA) by using wearable devic...
Surveys and Questionnaires Wearable Electronic Devices Aged Ecological Momentary Assessment Logistic Models Aged, 80 and over Residence Characteristics Humans Male Machine Learning Depression Republic of Korea Female
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