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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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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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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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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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Classification of radiographic lung pattern based on texture analysis and machine learning

Yoon Y, Hwang T, Choi H, Lee H

This study evaluated the feasibility of using texture analysis and machine learning to distinguish radiographic lung patterns. A total of 1200 regions of interest (ROIs) including four specific lung patterns...
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Prediction of survival outcomes in patients with epithelial ovarian cancer using machine learning methods

Paik ES, Lee JW, Park JY, Kim JH, Kim M, Kim TJ, Choi CH, Kim BG, Bae DS, Seo SW

OBJECTIVES: The aim of this study was to develop a new prognostic classification for epithelial ovarian cancer (EOC) patients using gradient boosting (GB) and to compare the accuracy of the...
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Prediction of Acquired Taxane Resistance Using a Personalized Pathway-Based Machine Learning Method

Kim YR, Kim D, Kim SY

PURPOSE: This study was conducted to develop and validate an individualized prediction model for automated detection of acquired taxane resistance (ATR). MATERIALS AND METHODS: Penalized regression, combinedwith an individualized pathway score...
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Development of Predictive Models in Patients with Epiphora Using Lacrimal Scintigraphy and Machine Learning

Park YJ, Bae JH, Shin MH, Hyun SH, Cho YS, Choe YS, Choi JY, Lee KH, Kim BT, Moon SH

PURPOSE: We developed predictive models using different programming languages and different computing platforms for machine learning (ML) and deep learning (DL) that classify clinical diagnoses in patients with epiphora. We...
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Review of Machine Learning Algorithms for Diagnosing Mental Illness

Cho G, Yim J, Choi Y, Ko J, Lee SH

OBJECTIVE: Enhanced technology in computer and internet has driven scale and quality of data to be improved in various areas including healthcare sectors. Machine Learning (ML) has played a pivotal...
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Predicting the Number of People for Meals of an Institutional Foodservice by Applying Machine Learning Methods: S City Hall Case

Jeon J, Park E, Kwon O

Predicting the number of meals in a foodservice organization is an important decision-making process that is essential for successful food production, such as reducing the amount of residue, preventing menu...
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Machine Learning for the Prediction of New-Onset Diabetes Mellitus during 5-Year Follow-up in Non-Diabetic Patients with Cardiovascular Risks

Choi BG, Rha SW, Kim SW, Kang JH, Park JY, Noh YK

PURPOSE: Many studies have proposed predictive models for type 2 diabetes mellitus (T2DM). However, these predictive models have several limitations, such as user convenience and reproducibility. The purpose of this...
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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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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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