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| 1 |
Understanding Arteriosclerotic Heart Disease Patients Using Electronic Health Records: A Machine Learning and Shapley Additive exPlanations Approach
Eka Miranda, Suko Adiarto, Faqir M. Bhatti, Alfi Yusrotis Zakiyyah, Mediana Aryuni, Charles Bernando
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| 2 |
Open datasets in perioperative medicine: a narrative review
Leerang Lim, Hyung-Chul Lee
Anesth Pain Med.2023;18(3):213-219. Published online 2023 July 26
DOI: http://dx.doi.org/10.17085/apm.23076
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| 3 |
기계학습 알고리즘을 이용한 간섬유화 바이오마커의 검증
Min-Kyeong Kim, Heon-Ju Kwon, Kangsu Shin, Hyosoon Park, Hee-Yeon Woo, Chang-Hun Park, Min-Jung Kwon
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| 4 |
Prediction of medication-related osteonecrosis of the jaw (MRONJ) using automated machine learning in patients with osteoporosis associated with dental extraction and implantation: a retrospective study
Da Woon Kwack, Sung Min Park
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| 5 |
Machine Learning-Based Proteomics Reveals Ferroptosis in COPD Patient-Derived Airway Epithelial Cells Upon Smoking Exposure
Jung-Ki Yoon, Sungjoon Park, Kyoung-Hee Lee, Dabin Jeong, Jisu Woo, Jieun Park, Seung-Muk Yi, Dohyun Han, Chul-Gyu Yoo, Sun Kim, Chang-Hoon Lee
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| 6 |
Machine-Learning Based Analysis of Usefulness of Wideband Tympanometry in Various Middle Ear Disorders
Seung Cheol Han, Hae Chan Park, Ye Jin Byun, Myung-Whan Suh, Jun Ho Lee, Seung-Ha Oh, Moo Kyun Park
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| 7 |
A Prediction Model for Osteoporosis Risk Using a Machine-Learning Approach and Its Validation in a Large Cohort
Xuangao Wu, Sunmin Park
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| 8 |
IPMN-LEARN: A linear support vector machine learning model for predicting low-grade intraductal papillary mucinous neoplasms
Yasmin Genevieve Hernandez-Barco, Dania Daye, Carlos F. Fernandez-del Castillo, Regina F. Parker, Brenna W. Casey, Andrew L. Warshaw, Cristina R. Ferrone, Keith D. Lillemoe, Motaz Qadan
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| 9 |
Data Modeling Using Vital Sign Dynamics for In-hospital Mortality Classification in Patients with Acute Coronary Syndrome
Sarawuth Limprasert, Ajchara Phu-ang
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| 10 |
Design of a Machine Learning System to Predict the Thickness of a Melanoma Lesion in a Non-Invasive Way from Dermoscopic Images
Ádám Szijártó, Ellák Somfai, András Lőrincz
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| 11 |
Feasibility Study of Federated Learning on the Distributed Research Network of OMOP Common Data Model
Geun Hyeong Lee, Jonggul Park, Jihyeong Kim, Yeesuk Kim, Byungjin Choi, Rae Woong Park, Sang Youl Rhee, Soo-Yong Shin
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| 12 |
Improvement of Dialysis Dosing Using Big Data Analytics
Syeda Leena Mumtaz, Abdulrahim Shamayleh, Hussam Alshraideh, Adnane Guella
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| 13 |
A machine learning based decision tree analysis of influential factor for the number of remaining teeth in Korean adults
Su-Yeon Hwang, Jung-Eun Park
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| 14 |
Identifying Disease of Interest With Deep Learning Using Diagnosis Code
Yoon-Sik Cho, Eunsun Kim, Patrick L. Stafford, Min-hwan Oh, Younghoon Kwon
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| 15 |
Machine Learning Algorithms for Predicting Treatment Outcomes of Oropharyngeal Cancer After Surgery
Dachan Kim, Se-Heon Kim, Eun Chang Choi, Jae-Yol Lim, Yoon Woo Koh, Young Min Park
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| 16 |
A Classifying Model of Obstructive Sleep Apnea Based on Heart Rate Variability in a Large Korean Population
Pona Park, Jeong-Whun Kim
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| 17 |
Clinical Decision Support System for Geriatric Dental Treatment Using a Bayesian Network and a Convolutional Neural Network
Bhornsawan Thanathornwong, Siriwan Suebnukarn, Kan Ouivirach
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| 18 |
Machine Learning-based Classifiers for the Prediction of Low Birth Weight
Mahya Arayeshgari, Somayeh Najafi-Ghobadi, Hosein Tarhsaz, Sharareh Parami, Leili Tapak
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| 19 |
Machine Learning Approach to Drug Treatment Strategy for Diabetes Care
Kazuya Fujihara, Hirohito Sone
Diabetes Metab J.2023;47(3):325-332. Published online 2023 January 12
DOI: http://dx.doi.org/10.4093/dmj.2022.0349
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| 20 |
Prediction of Diabetic Neuropathy Using Machine Learning Techniques
Jung Keun Hyun
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