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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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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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First Report of Buchwaldoboletus lignicola (Boletaceae), a Potentially Endangered Basidiomycete Species, in South Korea

Jo JW, Kwag YN, Cho SE, Han SK, Han JG, Lim YW, Sung GH, Oh SH, Kim CS

During the 2014 survey of the mushroom flora of Gwangneung forest in South Korea, we collected two specimens of boletoid mushroom growing on a felled tree of Pinus koraiensis. These...
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Effect of Acaromyces Ingoldii Secondary Metabolites on the Growth of Brown-Rot (Gloeophyllum Trabeum) and White-Rot (Trametes Versicolor) Fungi

Olatinwo R, So CL, Eberhardt

We investigated the antifungal activities of an endophytic fungus identified as Acaromyces ingoldii, found on a loblolly (Pinus taeda L.) pine bolt in Louisiana during routine laboratory microbial isolations. The...
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A Survey of Termitomyces (Lyophyllaceae, Agaricales), Including a New Species, from a Subtropical Forest in Xishuangbanna, China

Ye L, Karunarathna SC, Li H, Xu J, Hyde KD, Mortimer P

A survey of mushrooms was conducted in Xishuangbanna, Yunnan Province, China, in the rainy season (May to October) of 2012, 2013, and 2014, during which 16 specimens of Termitomyces were...
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Four Year Surveillance of the Vector Hard Ticks for SFTS, Ganghwa-do, Republic of Korea

Kim-Jeon M, Jegal S, Jun H, Jung H, Park SH, Ahn SK, Lee J, Gong YW, Joo K, Kwon MJ, Roh JY, Lee WG, Bahk YY, Kim TS

The seasonal abundance of hard ticks that transmit severe fever with thrombocytopenia syndrome virus was monitored with a collection trap method every April to November during 2015–2018 and with a...
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Machine Learning-Based Prediction of Korean Triage and Acuity Scale Level in Emergency Department Patients

Choi SW, Ko T, Hong KJ, Kim KH

OBJECTIVES: Triage is a process to accurately assess and classify symptoms to identify and provide rapid treatment to patients. The Korean Triage and Acuity Scale (KTAS) is used as a...
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Stacking Ensemble Technique for Classifying Breast Cancer

Kwon H, Park J, Lee Y

OBJECTIVES: Breast cancer is the second most common cancer among Korean women. Because breast cancer is strongly associated with negative emotional and physical changes, early detection and treatment of breast...
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Pullout Strength Predictor: A Machine Learning Approach

Khatri R, Varghese V, Sharma S, Kumar GS, Chhabra HS

STUDY DESIGN: A biomechanical study. PURPOSE: To develop a predictive model for pullout strength. OVERVIEW OF LITERATURE: Spine fusion surgeries are performed to correct joint deformities by restricting motion between two or...
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Antifungal Activities of Streptomyces blastmyceticus Strain 12-6 Against Plant Pathogenic Fungi

Kim YJ, Kim JH, Rho JY

Streptomyces blastmyceticus strain 12-6 was isolated from a forest soil sample of Cheonan area on the basis of strong antifungal activities against plant pathogenic fungi. Butanol extracts of the cultural...
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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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Classification of Common Relationships Based on Short Tandem Repeat Profiles Using Data Mining

Jeong SJ, Lee HJ, Lee SD, Lee SH, Park SJ, Kim JS, Lee JW

We reviewed past studies on the identification of familial relationships using 22 short tandem repeat markers. As a result, we can obtain a high discrimination power and a relatively accurate...
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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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Perceptual and Acoustic Outcomes of Early-Stage Glottic Cancer After Laser Surgery or Radiotherapy: A Meta-Analysis

Lee SH, Hong KH, Kim JS, Hong YT

Laser surgery (LS) or radiotherapy (RT) is normally recommended in early glottic cancer. The objective of this study was to perform a comprehensive meta-analysis of acoustic and perceptual outcomes to...
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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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Using big data to see the forest and the trees: endoscopic submucosal dissection of early gastric cancer in Korea

Bang CS, Baik GH

No abstract available.
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Effects of Outdoor Activities in Forests on Atopic Dermatitis

Kim WK, Kim W, Woo JM

BACKGROUND: The aim of this study was to determine the effects of out-door activities in the forest environment, the so-called "forest therapy program," among children with atopic dermatitis (AD). METHODS: A...
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Development and Validation of Deep-Learning Algorithm for Electrocardiography-Based Heart Failure Identification

Kwon JM, Kim KH, Jeon KH, Kim HM, Kim MJ, Lim SM, Song PS, Park J, Choi RK, Oh BH

BACKGROUND AND OBJECTIVES: Screening and early diagnosis for heart failure (HF) are critical. However, conventional screening diagnostic methods have limitations, and electrocardiography (ECG)-based HF identification may be helpful. This study...
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Let's Not Miss the Forest for the Trees

Hyun SJ

No abstract available.
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