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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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Artificial Intelligence for Adult Spinal Deformity

Joshi RS, Haddad AF, Lau D, Ames CP

Adult spinal deformity (ASD) is a complex disease that significantly affects the lives of many patients. Surgical correction has proven to be effective in achieving improvement of spinopelvic parameters as...
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Predictive Modeling of Outcomes After Traumatic and Nontraumatic Spinal Cord Injury Using Machine Learning: Review of Current Progress and Future Directions

Khan O, Badhiwala , Wilson JR, Jiang F, Martin AR, Fehlings M

Machine learning represents a promising frontier in epidemiological research on spine surgery. It consists of a series of algorithms that determines relationships between data. Machine learning maintains numerous advantages over...
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Predictive Analytics in Spine Oncology Research: First Steps, Limitations, and Future Directions

Massaad E, Fatima N, Hadzipasic M, Alvarez-Breckenridge C, Shankar GM, Shin JH

The potential of big data analytics to improve the quality of care for patients with spine tumors is significant. At this moment, the application of big data analytics to oncology...
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Deep Learning in Medical Imaging

Kim M, Yun J, Cho Y, Shin K, Jang R, Bae HJ, Kim N

The artificial neural network (ANN), one of the machine learning (ML) algorithms, inspired by the human brain system, was developed by connecting layers with artificial neurons. However, due to the...
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Data Mining in Spine Surgery: Leveraging Electronic Health Records for Machine Learning and Clinical Research

Staartjes , Stienen MN

No abstract available.
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Applications of Machine Learning Using Electronic Medical Records in Spine Surgery

Schwartz J, Gao M, Geng EA, Mody KS, Mikhail CM, Cho SK

Developments in machine learning in recent years have precipitated a surge in research on the applications of artificial intelligence within medicine. Machine learning algorithms are beginning to impact medicine broadly,...
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Review of statistical methods for survival analysis using genomic data

Lee S, Lim H

Survival analysis mainly deals with the time to event, including death, onset of disease, and bankruptcy. The common characteristic of survival analysis is that it contains “censored” data, in which...
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The extension of the largest generalized-eigenvalue based distance metric D(ij)(γ₁) in arbitrary feature spaces to classify composite data points

Daoud M

Analyzing patterns in data points embedded in linear and non-linear feature spaces is considered as one of the common research problems among different research areas, for example: data mining, machine...
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Volumetric-Modulated Arc Radiotherapy Using Knowledge-Based Planning: Application to Spine Stereotactic Body Radiotherapy

Jeong C, Park JW, Kwak J, Song SY, Cho B

PURPOSE: To evaluate the clinical feasibility of knowledge-based planning (KBP) for volumetric-modulated arc radiotherapy (VMAT) in spine stereotactic body radiotherapy (SBRT). METHODS: Forty-eight VMAT plans for spine SBRT was studied. Two...
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Medical Big Data Is Not Yet Available: Why We Need Realism Rather than Exaggeration

Kim HS, Kim DJ, Yoon KH

Most people are now familiar with the concepts of big data, deep learning, machine learning, and artificial intelligence (AI) and have a vague expectation that AI using medical big data...
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Augmentation of Doppler Radar Data Using Generative Adversarial Network for Human Motion Analysis

Alnujaim I, Kim Y

OBJECTIVES: Human motion analysis can be applied to the diagnosis of musculoskeletal diseases, rehabilitation therapies, fall detection, and estimation of energy expenditure. To analyze human motion with micro-Doppler signatures measured...
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