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Review Articles
Calcium & Bone Metabolism
Applications of Machine Learning in Bone and Mineral Research
Sung Hye Kong, Chan Soo Shin
Endocrinol Metab. 2021;36(5):928-937.   Published online October 21, 2021
DOI: https://doi.org/10.3803/EnM.2021.1111
  • 4,764 View
  • 179 Download
  • 8 Web of Science
  • 7 Crossref
AbstractAbstract PDFPubReader   ePub   
In this unprecedented era of the overwhelming volume of medical data, machine learning can be a promising tool that may shed light on an individualized approach and a better understanding of the disease in the field of osteoporosis research, similar to that in other research fields. This review aimed to provide an overview of the latest studies using machine learning to address issues, mainly focusing on osteoporosis and fractures. Machine learning models for diagnosing and classifying osteoporosis and detecting fractures from images have shown promising performance. Fracture risk prediction is another promising field of research, and studies are being conducted using various data sources. However, these approaches may be biased due to the nature of the techniques or the quality of the data. Therefore, more studies based on the proposed guidelines are needed to improve the technical feasibility and generalizability of artificial intelligence algorithms.

Citations

Citations to this article as recorded by  
  • Predicting postoperative outcomes in lumbar spinal fusion: development of a machine learning model
    Lukas Schönnagel, Thomas Caffard, Tu-Lan Vu-Han, Jiaqi Zhu, Isaac Nathoo, Kyle Finos, Gaston Camino-Willhuber, Soji Tani, Ali. E. Guven, Henryk Haffer, Maximilian Muellner, Artine Arzani, Erika Chiapparelli, Krizia Amoroso, Jennifer Shue, Roland Duculan,
    The Spine Journal.2024; 24(2): 239.     CrossRef
  • A CT-based Deep Learning Model for Predicting Subsequent Fracture Risk in Patients with Hip Fracture
    Yisak Kim, Young-Gon Kim, Jung-Wee Park, Byung Woo Kim, Youmin Shin, Sung Hye Kong, Jung Hee Kim, Young-Kyun Lee, Sang Wan Kim, Chan Soo Shin
    Radiology.2024;[Epub]     CrossRef
  • Applying machine learning classification techniques for disease diagnosis from medical imaging data using Transformer based Attention Guided CNN (TAGCNN)
    Saleh Alyahyan
    Multimedia Tools and Applications.2024;[Epub]     CrossRef
  • Development and validation of common data model-based fracture prediction model using machine learning algorithm
    Sung Hye Kong, Sihyeon Kim, Yisak Kim, Jung Hee Kim, Kwangsoo Kim, Chan Soo Shin
    Osteoporosis International.2023; 34(8): 1437.     CrossRef
  • Machine learning algorithms for predicting the risk of fracture in patients with diabetes in China
    Sijia Chu, Aijun Jiang, Lyuzhou Chen, Xi Zhang, Xiurong Shen, Wan Zhou, Shandong Ye, Chao Chen, Shilu Zhang, Li Zhang, Yang Chen, Ya Miao, Wei Wang
    Heliyon.2023; 9(7): e18186.     CrossRef
  • Leveraging Artificial Intelligence and Machine Learning in Regenerative Orthopedics: A Paradigm Shift in Patient Care
    Madhan Jeyaraman, Harish V K Ratna, Naveen Jeyaraman, Aakaash Venkatesan, Swaminathan Ramasubramanian , Sankalp Yadav
    Cureus.2023;[Epub]     CrossRef
  • Quality use of artificial intelligence in medical imaging: What do radiologists need to know?
    Stacy K Goergen, Helen ML Frazer, Sandeep Reddy
    Journal of Medical Imaging and Radiation Oncology.2022; 66(2): 225.     CrossRef
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Miscellaneous
Medical Big Data Is Not Yet Available: Why We Need Realism Rather than Exaggeration
Hun-Sung Kim, Dai-Jin Kim, Kun-Ho Yoon
Endocrinol Metab. 2019;34(4):349-354.   Published online December 23, 2019
DOI: https://doi.org/10.3803/EnM.2019.34.4.349
  • 5,840 View
  • 140 Download
  • 36 Web of Science
  • 47 Crossref
AbstractAbstract PDFPubReader   ePub   

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 can be used to improve the quality of medical care. However, the expectation that big data could change the field of medicine is inconsistent with the current reality. The clinical meaningfulness of the results of research using medical big data needs to be examined. Medical staff needs to be clear about the purpose of AI that utilizes medical big data and to focus on the quality of this data, rather than the quantity. Further, medical professionals should understand the necessary precautions for using medical big data, as well as its advantages. No doubt that someday, medical big data will play an essential role in healthcare; however, at present, it seems too early to actively use it in clinical practice. The field continues to work toward developing medical big data and making it appropriate for healthcare. Researchers should continue to engage in empirical research to ensure that appropriate processes are in place to empirically evaluate the results of its use in healthcare.

Citations

Citations to this article as recorded by  
  • Current status of remote collaborative care for hypertension in medically underserved areas
    Seo Yeon Baik, Kyoung Min Kim, Hakyoung Park, Jiwon Shinn, Hun-Sung Kim
    Cardiovascular Prevention and Pharmacotherapy.2024; 6(1): 33.     CrossRef
  • Prediction of Cardiovascular Complication in Patients with Newly Diagnosed Type 2 Diabetes Using an XGBoost/GRU-ODE-Bayes-Based Machine-Learning Algorithm
    Joonyub Lee, Yera Choi, Taehoon Ko, Kanghyuck Lee, Juyoung Shin, Hun-Sung Kim
    Endocrinology and Metabolism.2024; 39(1): 176.     CrossRef
  • Dark Data in Real-World Evidence: Challenges, Implications, and the Imperative of Data Literacy in Medical Research
    Hun-Sung Kim
    Journal of Korean Medical Science.2024;[Epub]     CrossRef
  • A comparative analysis: health data protection laws in Malaysia, Saudi Arabia and EU General Data Protection Regulation (GDPR)
    Jawahitha Sarabdeen, Mohamed Mazahir Mohamed Ishak
    International Journal of Law and Management.2024;[Epub]     CrossRef
  • Long-Term Risk of Cardiovascular Disease Among Type 2 Diabetes Patients According to Average and Visit-to-Visit Variations of HbA1c Levels During the First 3 Years of Diabetes Diagnosis
    Hyunah Kim, Da Young Jung, Seung-Hwan Lee, Jae-Hyoung Cho, Hyeon Woo Yim, Hun-Sung Kim
    Journal of Korean Medical Science.2023;[Epub]     CrossRef
  • Comparison of cardiocerebrovascular disease incidence between angiotensin converting enzyme inhibitor and angiotensin receptor blocker users in a real-world cohort
    Suehyun Lee, Hyunah Kim, Hyeon Woo Yim, Kim Hun-Sung, Ju Han Kim
    Journal of Applied Biomedicine.2023; 21(1): 7.     CrossRef
  • Multi-Omics and Management of Follicular Carcinoma of the Thyroid
    Thifhelimbilu Emmanuel Luvhengo, Ifongo Bombil, Arian Mokhtari, Maeyane Stephens Moeng, Demetra Demetriou, Claire Sanders, Zodwa Dlamini
    Biomedicines.2023; 11(4): 1217.     CrossRef
  • Correlation analysis of cancer incidence after pravastatin treatment
    Jin Yu, Raeun Kim, Jiwon Shinn, Man Young Park, Hun-Sung Kim
    Cardiovascular Prevention and Pharmacotherapy.2023; 5(2): 61.     CrossRef
  • A New Strategy for Evaluating the Quality of Laboratory Results for Big Data Research: Using External Quality Assessment Survey Data (2010–2020)
    Eun-Jung Cho, Tae-Dong Jeong, Sollip Kim, Hyung-Doo Park, Yeo-Min Yun, Sail Chun, Won-Ki Min
    Annals of Laboratory Medicine.2023; 43(5): 425.     CrossRef
  • Weight loss and side-effects of liraglutide and lixisenatide in obesity and type 2 diabetes mellitus
    Jeongmin Lee, Raeun Kim, Min-Hee Kim, Seung-Hwan Lee, Jae-Hyoung Cho, Jung Min Lee, Sang-Ah Jang, Hun-Sung Kim
    Primary Care Diabetes.2023; 17(5): 460.     CrossRef
  • Cohort profile for development of machine learning models to predict healthcare-related adverse events (Demeter): clinical objectives, data requirements for modelling and overview of data set for 2016–2018
    Svetlana Artemova, Ursula von Schenck, Rui Fa, Daniel Stoessel, Hadiseh Nowparast Rostami, Pierre-Ephrem Madiot, Jean-Marie Januel, Daniel Pagonis, Caroline Landelle, Meghann Gallouche, Christophe Cancé, Frederic Olive, Alexandre Moreau-Gaudry, Sigurd Pri
    BMJ Open.2023; 13(8): e070929.     CrossRef
  • The Present and Future of Artificial Intelligence-Based Medical Image in Diabetes Mellitus: Focus on Analytical Methods and Limitations of Clinical Use
    Ji-Won Chun, Hun-Sung Kim
    Journal of Korean Medical Science.2023;[Epub]     CrossRef
  • Construction and application on the training course of information literacy for clinical nurses
    Chao Wu, Yinjuan Zhang, Jing Wu, Linyuan Zhang, Juan Du, Lu Li, Nana Chen, Liping Zhu, Sheng Zhao, Hongjuan Lang
    BMC Medical Education.2023;[Epub]     CrossRef
  • Lightweight Histological Tumor Classification Using a Joint Sparsity-Quantization Aware Training Framework
    Dina Aboutahoun, Rami Zewail, Keiji Kimura, Mostafa I. Soliman
    IEEE Access.2023; 11: 119342.     CrossRef
  • Long-Term Cumulative Exposure to High γ-Glutamyl Transferase Levels and the Risk of Cardiovascular Disease: A Nationwide Population-Based Cohort Study
    Han-Sang Baek, Bongseong Kim, Seung-Hwan Lee, Dong-Jun Lim, Hyuk-Sang Kwon, Sang-Ah Chang, Kyungdo Han, Jae-Seung Yun
    Endocrinology and Metabolism.2023; 38(6): 770.     CrossRef
  • Comorbidity Patterns and Management in Inpatients with Endocrine Diseases by Age Groups in South Korea: Nationwide Data
    Sung-Soo Kim, Hun-Sung Kim
    Journal of Personalized Medicine.2023; 14(1): 42.     CrossRef
  • Angiotensin‐converting enzyme inhibitors versus angiotensin receptor blockers: New‐onset diabetes mellitus stratified by statin use
    Juyoung Shin, Hyunah Kim, Hyeon Woo Yim, Ju Han Kim, Suehyun Lee, Hun‐Sung Kim
    Journal of Clinical Pharmacy and Therapeutics.2022; 47(1): 97.     CrossRef
  • Physician Knowledge Base: Clinical Decision Support Systems
    Sira Kim, Eung-Hee Kim, Hun-Sung Kim
    Yonsei Medical Journal.2022; 63(1): 8.     CrossRef
  • Sodium-Glucose Cotransporter-2 Inhibitor-Related Diabetic Ketoacidosis: Accuracy Verification of Operational Definition
    Dong Yoon Kang, Hyunah Kim, SooJeong Ko, HyungMin Kim, Jiwon Shinn, Min-Gyu Kang, Sun-ju Byeon, Jeong-Hee Choi, Soo-Yong Shin, Hun-Sung Kim
    Journal of Korean Medical Science.2022;[Epub]     CrossRef
  • Drug Repositioning: Exploring New Indications for Existing Drug-Disease Relationships
    Hun-Sung Kim
    Endocrinology and Metabolism.2022; 37(1): 62.     CrossRef
  • A Study on Methodologies of Drug Repositioning Using Biomedical Big Data: A Focus on Diabetes Mellitus
    Suehyun Lee, Seongwoo Jeon, Hun-Sung Kim
    Endocrinology and Metabolism.2022; 37(2): 195.     CrossRef
  • Development of a predictive model for the side effects of liraglutide
    Jiyoung Min, Jiwon Shinn, Hun-Sung Kim
    Cardiovascular Prevention and Pharmacotherapy.2022; 4(2): 87.     CrossRef
  • Understanding and Utilizing Claim Data from the Korean National Health Insurance Service (NHIS) and Health Insurance Review & Assessment (HIRA) Database for Research
    Dae-Sung Kyoung, Hun-Sung Kim
    Journal of Lipid and Atherosclerosis.2022; 11(2): 103.     CrossRef
  • The Impact of the Association between Cancer and Diabetes Mellitus on Mortality
    Sung-Soo Kim, Hun-Sung Kim
    Journal of Personalized Medicine.2022; 12(7): 1099.     CrossRef
  • Development of Various Diabetes Prediction Models Using Machine Learning Techniques
    Juyoung Shin, Jaewon Kim, Chanjung Lee, Joon Young Yoon, Seyeon Kim, Seungjae Song, Hun-Sung Kim
    Diabetes & Metabolism Journal.2022; 46(4): 650.     CrossRef
  • Characteristics of Glycemic Control and Long-Term Complications in Patients with Young-Onset Type 2 Diabetes
    Han-sang Baek, Ji-Yeon Park, Jin Yu, Joonyub Lee, Yeoree Yang, Jeonghoon Ha, Seung Hwan Lee, Jae Hyoung Cho, Dong-Jun Lim, Hun-Sung Kim
    Endocrinology and Metabolism.2022; 37(4): 641.     CrossRef
  • Retrospective cohort analysis comparing changes in blood glucose level and body composition according to changes in thyroid‐stimulating hormone level
    Hyunah Kim, Da Young Jung, Seung‐Hwan Lee, Jae‐Hyoung Cho, Hyeon Woo Yim, Hun‐Sung Kim
    Journal of Diabetes.2022; 14(9): 620.     CrossRef
  • Long-Term Changes in HbA1c According to Blood Glucose Control Status During the First 3 Months After Visiting a Tertiary University Hospital
    Hyunah Kim, Da Young Jung, Seung-Hwan Lee, Jae-Hyoung Cho, Hyeon Woo Yim, Hun-Sung Kim
    Journal of Korean Medical Science.2022;[Epub]     CrossRef
  • Medication based machine learning to identify subpopulations of pediatric hemodialysis patients in an electronic health record database
    Autumn M. McKnite, Kathleen M. Job, Raoul Nelson, Catherine M.T. Sherwin, Kevin M. Watt, Simon C. Brewer
    Informatics in Medicine Unlocked.2022; 34: 101104.     CrossRef
  • Improving Machine Learning Diabetes Prediction Models for the Utmost Clinical Effectiveness
    Juyoung Shin, Joonyub Lee, Taehoon Ko, Kanghyuck Lee, Yera Choi, Hun-Sung Kim
    Journal of Personalized Medicine.2022; 12(11): 1899.     CrossRef
  • A Study on Weight Loss Cause as per the Side Effect of Liraglutide
    Jin Yu, Jeongmin Lee, Seung-Hwan Lee, Jae-Hyung Cho, Hun-Sung Kim, Heng Zhou
    Cardiovascular Therapeutics.2022; 2022: 1.     CrossRef
  • Risk Classification and Subphenotyping of Acute Kidney Injury: Concepts and Methodologies
    Javier A. Neyra, Jin Chen, Sean M. Bagshaw, Jay L. Koyner
    Seminars in Nephrology.2022; 42(3): 151285.     CrossRef
  • Estimation of sodium‐glucose cotransporter 2 inhibitor–related genital and urinary tract infections via electronic medical record–based common data model
    SooJeong Ko, HyungMin Kim, Jiwon Shinn, Sun‐ju Byeon, Jeong‐Hee Choi, Hun‐Sung Kim
    Journal of Clinical Pharmacy and Therapeutics.2021; 46(4): 975.     CrossRef
  • Blood glucose levels and bodyweight change after dapagliflozin administration
    Hyunah Kim, Seung‐Hwan Lee, Hyunyong Lee, Hyeon Woo Yim, Jae‐Hyoung Cho, Kun‐Ho Yoon, Hun‐Sung Kim
    Journal of Diabetes Investigation.2021; 12(9): 1594.     CrossRef
  • Artificial intelligence in healthcare: possibilities of patent protection
    T. N. Erivantseva, Yu. V. Blokhina
    FARMAKOEKONOMIKA. Modern Pharmacoeconomic and Pharmacoepidemiology.2021; 14(2): 270.     CrossRef
  • Lack of Acceptance of Digital Healthcare in the Medical Market: Addressing Old Problems Raised by Various Clinical Professionals and Developing Possible Solutions
    Jong Il Park, Hwa Young Lee, Hyunah Kim, Jisan Lee, Jiwon Shinn, Hun-Sung Kim
    Journal of Korean Medical Science.2021;[Epub]     CrossRef
  • Prospect of Artificial Intelligence Based on Electronic Medical Records
    Suehyun Lee, Hun-Sung Kim
    Journal of Lipid and Atherosclerosis.2021; 10(3): 282.     CrossRef
  • Data Pseudonymization in a Range That Does Not Affect Data Quality: Correlation with the Degree of Participation of Clinicians
    Soo-Yong Shin, Hun-Sung Kim
    Journal of Korean Medical Science.2021;[Epub]     CrossRef
  • Development of a Predictive Model for Glycated Hemoglobin Values and Analysis of the Factors Affecting It
    HyeongKyu Park, Da Young Lee, So young Park, Jiyoung Min, Jiwon Shinn, Dae Ho Lee, Soon Hyo Kwon, Hun-Sung Kim, Nan Hee Kim
    Cardiovascular Prevention and Pharmacotherapy.2021; 3(4): 106.     CrossRef
  • Modeling of Changes in Creatine Kinase after HMG-CoA Reductase Inhibitor Prescription
    Hun-Sung Kim, Jiyoung Min, Jiwon Shinn, Oak-Kee Hong, Jang-Won Son, Seong-Su Lee, Sung-Rae Kim, Soon Jib Yoo
    Cardiovascular Prevention and Pharmacotherapy.2021; 3(4): 115.     CrossRef
  • TRAINING IN BIG DATA TECHNOLOGIES OF MEDICAL UNIVERSITY STUDENTS
    K.S ITINSON
    AZIMUTH OF SCIENTIFIC RESEARCH: PEDAGOGY AND PSYCHOLOGY.2021;[Epub]     CrossRef
  • Machine Learning Applications in Endocrinology and Metabolism Research: An Overview
    Namki Hong, Heajeong Park, Yumie Rhee
    Endocrinology and Metabolism.2020; 35(1): 71.     CrossRef
  • Lessons from Use of Continuous Glucose Monitoring Systems in Digital Healthcare
    Hun-Sung Kim, Kun-Ho Yoon
    Endocrinology and Metabolism.2020; 35(3): 541.     CrossRef
  • Apprehensions about Excessive Belief in Digital Therapeutics: Points of Concern Excluding Merits
    Hun-Sung Kim
    Journal of Korean Medical Science.2020;[Epub]     CrossRef
  • Medical Ethics in the Era of Artificial Intelligence Based on Medical Big Data
    Hae-Ran Na, Hun-Sung Kim
    The Journal of Korean Diabetes.2020; 21(3): 126.     CrossRef
  • Machine Learning Application in Diabetes and Endocrine Disorders
    Namki Hong, Heajeong Park, Yumie Rhee
    The Journal of Korean Diabetes.2020; 21(3): 130.     CrossRef
  • Real World Data and Artificial Intelligence in Diabetology
    Kwang Joon Kim
    The Journal of Korean Diabetes.2020; 21(3): 140.     CrossRef
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Endocrinol Metab : Endocrinology and Metabolism