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Understanding the Molecular Mechanisms of Asthma through Transcriptomics

Park HW, Weiss ST

The transcriptome represents the complete set of RNA transcripts that are produced by the genome under a specific circumstance or in a specific cell. High-throughput methods, including microarray and bulk...
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Prediction of dental caries in 12-year-old children using machine-learning algorithms

Yang YH, Kim JS, Jeong SH

OBJECTIVES: The decayed-missing-filled (DMFT) index is a representative oral health indicator. Prediction of DMFT index is an important basis for the development of public oral health care projects and strategies...
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Deep Learning in Upper Gastrointestinal Disorders: Status and Future Perspectives

Bang CS

Artificial intelligence using deep learning has been applied to gastrointestinal disorders for the detection, classification, and delineation of various lesion images. With the accumulation of enormous medical records, the evolution...
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Machine Learning Applications in Endocrinology and Metabolism Research: An Overview

Hong N, Park H, Rhee Y

Machine learning (ML) applications have received extensive attention in endocrinology research during the last decade. This review summarizes the basic concepts of ML and certain research topics in endocrinology and...
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Future Directions in Coronary CT Angiography: CT-Fractional Flow Reserve, Plaque Vulnerability, and Quantitative Plaque Assessment

Kay FU, Canan A, Abbara S

Coronary computed tomography angiography (CCTA) is a well-validated and noninvasive imaging modality for the assessment of coronary artery disease (CAD) in patients with stable ischemic heart disease and acute coronary...
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Hierarchical Cluster Analysis of Peripapillary Retinal Nerve Fiber Layer Damage and Macular Ganglion Cell Loss in Open Angle Glaucoma

Lee K, Bae HW, Lee SY, Seong GJ, Kim CY

PURPOSE: To categorize the structural progression pattern of glaucoma, as detected by optical coherence tomography guided progression analysis, with respect to the peripapillary retinal nerve fiber layer (RNFL) and macular...
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Prediction of Chronic Disease-Related Inpatient Prolonged Length of Stay Using Machine Learning Algorithms

Symum H, Zayas-Castro JL

OBJECTIVES: The study aimed to develop and compare predictive models based on supervised machine learning algorithms for predicting the prolonged length of stay (LOS) of hospitalized patients diagnosed with five...
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Machine Learning and Initial Nursing Assessment-Based Triage System for Emergency Department

Yu JY, Jeong GY, Jeong OS, Chang DK, Cha WC

OBJECTIVES: The aim of this study was to develop machine learning (ML) and initial nursing assessment (INA)-based emergency department (ED) triage to predict adverse clinical outcome. METHODS: The retrospective study included...
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Machine Learning: a New Opportunity for Risk Prediction

Kwon O, Na W, Kim YH

No abstract available.
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Data-Driven Machine-Learning Quantifies Differences in the Voiding Initiation Network in Neurogenic Voiding Dysfunction in Women With Multiple Sclerosis

Karmonik C, Boone T, Khavari R

PURPOSE: To quantify the relative importance of brain regions responsible for reduced functional connectivity (FC) in their Voiding Initiation Network in female multiple sclerosis (MS) patients with neurogenic lower urinary...
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Epigenetics and Depression: An Update

Lin E, Tsai SJ

OBJECTIVE: Depression is associated with various environmental risk factors such as stress, childhood maltreatment experiences, and stressful life events. Current approaches to assess the pathophysiology of depression, such as epigenetics...
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Application of Artificial Intelligence in Lung Cancer Screening

Lee SM, Park CM

Lung cancer is a leading cause of deaths due to cancer, worldwide. At present, low-dose computed tomography (CT) is the only established screening method for reducing lung cancer mortality. However,...
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Toward a grey box approach for cardiovascular physiome

Hwang M, Leem CH, Shim EB

The physiomic approach is now widely used in the diagnosis of cardiovascular diseases. There are two possible methods for cardiovascular physiome: the traditional mathematical model and the machine learning (ML)...
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Radiomics MRI Phenotyping with Machine Learning to Predict the Grade of Lower-Grade Gliomas: A Study Focused on Nonenhancing Tumors

Park YW, Choi YS, Ahn SS, Chang JH, Kim SH, Lee SK

OBJECTIVE: To assess whether radiomics features derived from multiparametric MRI can predict the tumor grade of lower-grade gliomas (LGGs; World Health Organization grade II and grade III) and the nonenhancing...
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Machine Learning Approaches for the Prediction of Prostate Cancer according to Age and the Prostate-Specific Antigen Level

Lee J, Yang SW, Lee S, Hyon YK, Kim J, Jin L, Lee JY, Park JM, Ha T, Shin JH, Lim JS, Na YG, Song KH

PURPOSE: The aim of this study was to evaluate the applicability of machine learning methods that combine data on age and prostate-specific antigen (PSA) levels for predicting prostate cancer. MATERIALS AND...
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Detection of Suicide Attempters among Suicide Ideators Using Machine Learning

Ryu S, Lee H, Lee DK, Kim SW, Kim CE

OBJECTIVE: We aimed to develop predictive models to identify suicide attempters among individuals with suicide ideation using a machine learning algorithm. METHODS: Among 35,116 individuals aged over 19 years from the...
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Reproducibility and Generalizability in Radiomics Modeling: Possible Strategies in Radiologic and Statistical Perspectives

Park JE, Park SY, Kim HJ, Kim HS

Radiomics, which involves the use of high-dimensional quantitative imaging features for predictive purposes, is a powerful tool for developing and testing medical hypotheses. Radiologic and statistical challenges in radiomics include...
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Machine Learning Model to Predict Osteoporotic Spine with Hounsfield Units on Lumbar Computed Tomography

Nam KH, Seo I, Kim DH, Lee JI, Choi BK, Han IH

OBJECTIVE: Bone mineral density (BMD) is an important consideration during fusion surgery. Although dual X-ray absorptiometry is considered as the gold standard for assessing BMD, quantitative computed tomography (QCT) provides...
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Prediction and Staging of Hepatic Fibrosis in Children with Hepatitis C Virus: A Machine Learning Approach

Barakat NH, Barakat SH, Ahmed N

OBJECTIVES: The aim of this study is to develop an intelligent diagnostic system utilizing machine learning for data cleansing, then build an intelligent model and obtain new cutoff values for...
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Application of machine learning in rheumatic disease research

Kim KJ, Tagkopoulos I

Over the past decade, there has been a paradigm shift in how clinical data are collected, processed and utilized. Machine learning and artificial intelligence, fueled by breakthroughs in high-performance computing,...
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