- Applications of Machine Learning Using Electronic Medical Records in Spine Surgery
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Schwartz J, Gao M, Geng EA, Mody KS, Mikhail CM, Cho SK
- KMID: 2469037
- Neurospine.
- 2019 Dec;16(4):643-653.
- doi: 10.14245/ns.1938386.193
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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- A Review of Deep Genomics Applying Machine Learning in Genomic Medicine
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Kim TH
- KMID: 2424834
- Hanyang Med Rev.
- 2017 Nov;37(2):93-98.
- doi: 10.7599/hmr.2017.37.2.93
Genomic medicine is to determine how an individual's DNA alteration can affect the risk of various diseases and to understand mechanisms and design targeted treatments. Here, we focus on how... -
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- Artificial Neural Network: Understanding the Basic Concepts without Mathematics
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Han SH, Kim KW, Kim S, Youn YC
- KMID: 2442797
- Dement Neurocogn Disord.
- 2018 Sep;17(3):83-89.
- doi: 10.12779/dnd.2018.17.3.83
Machine learning is where a machine (i.e., computer) determines for itself how input data is processed and predicts outcomes when provided with new data. An artificial neural network is a... -
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- Application of machine learning in rheumatic disease research
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Kim KJ, Tagkopoulos I
- KMID: 2457303
- Korean J Intern Med.
- 2019 Jul;34(4):708-722.
- doi: 10.3904/kjim.2018.349
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 BMI Control Commands Using Extreme Learning Machine from Spike Trains of Simultaneously Recorded 34 CA1 Single Neural Signals
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Lee Y, Lee H, Lang Y, Kim J, Lee M, Shin HC
- KMID: 2165280
- Exp Neurobiol.
- 2008 Dec;17(2):33-39.
- doi: 10.5607/en.2008.17.2.33
A recently developed machine learning algorithm referred to as Extreme Learning Machine (ELM) was used to classify machine control commands out of time series of spike trains of ensembles of... -
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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
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Khan O, Badhiwala , Wilson JR, Jiang F, Martin AR, Fehlings M
- KMID: 2469041
- Neurospine.
- 2019 Dec;16(4):678-685.
- doi: 10.14245/ns.1938390.195
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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- Deep Learning in Nuclear Medicine and Molecular Imaging: Current Perspectives and Future Directions
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Choi H
- KMID: 2414798
- Nucl Med Mol Imaging.
- 2018 Apr;52(2):109-118.
- doi: 10.1007/s13139-017-0504-7
Recent advances in deep learning have impacted various scientific and industrial fields. Due to the rapid application of deep learning in biomedical data, molecular imaging has also started to adopt... -
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- Deep Learning in Upper Gastrointestinal Disorders: Status and Future Perspectives
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Bang CS
- KMID: 2471895
- Korean J Gastroenterol.
- 2020 Mar;75(3):120-131.
- doi: 10.4166/kjg.2020.75.3.120
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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- Data Mining in Spine Surgery: Leveraging Electronic Health Records for Machine Learning and Clinical Research
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Staartjes , Stienen MN
- KMID: 2469038
- Neurospine.
- 2019 Dec;16(4):654-656.
- doi: 10.14245/ns.1938434.217
No abstract available. -
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- PubMiner: Machine Learning-based Text Mining for Biomedical Information Analysis
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Eom JH, Zhang BT
- KMID: 2166172
- Genomics Inform.
- 2004 Jun;2(2):99-106.
In this paper we introduce PubMiner, an intelligent machine learning based text mining system for mining biological information from the literature. PubMiner employs natural language processing techniques and machine learning... -
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- Segmentation of Brain CT Image Machine Learning
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Kim HS, Lee YR
- KMID: 2210122
- J Korean Soc Med Inform.
- 1997 Dec;3(2):193-199.
A medical image segmentation is the primary issue in computer aided diagnosis. The traditional methods did not perform the image segmentation well because of varieties of image, inadequate informations, noises,... -
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- Machine Learning: a New Opportunity for Risk Prediction
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Kwon O, Na W, Kim YH
- KMID: 2465667
- Korean Circ J.
- 2020 Jan;50(1):85-87.
- doi: 10.4070/kcj.2019.0314
No abstract available. -
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- Prediction of dental caries in 12-year-old children using machine-learning algorithms
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Yang YH, Kim JS, Jeong SH
- KMID: 2471985
- J Korean Acad Oral Health.
- 2020 Mar;44(1):55-63.
- doi: 10.11149/jkaoh.2020.44.1.55
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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- Predicting the mortality of pneumonia patients visiting the emergency department through machine learning
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Bae Y, Moon HK, Kim SH
- KMID: 2435470
- J Korean Soc Emerg Med.
- 2018 Oct;29(5):455-464.
- doi: 10.0000/jksem.2018.29.5.455
OBJECTIVE: Machine learning is not yet widely used in the medical field. Therefore, this study was conducted to compare the performance of preexisting severity prediction models and machine learning based... -
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- Machine learning in biomedical engineering
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Park C, Took CC, Seong JK
- KMID: 2439530
- Biomed Eng Lett.
- 2018 Feb;8(1):1-3.
- doi: 10.1007/s13534-018-0058-3
No abstract available. -
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- Anesthesia research in the artificial intelligence era
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Lee HC, Jung CW
- KMID: 2436031
- Anesth Pain Med.
- 2018 Jul;13(3):248-255.
- doi: 10.17085/apm.2018.13.3.248
A noteworthy change in recent medical research is the rapid increase of research using big data obtained from electrical medical records (EMR), order communication systems (OCS), and picture archiving and... -
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- Deep Learning in the Medical Domain: Predicting Cardiac Arrest Using Deep Learning
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Lee Y, Kwon JM, Lee Y, Park H, Cho H, Park J
- KMID: 2436322
- Acute Crit Care.
- 2018 Aug;33(3):117-120.
- doi: 10.4266/acc.2018.00290
With the wider adoption of electronic health records, the rapid response team initially believed that mortalities could be significantly reduced but due to low accuracy and false alarms, the healthcare... -
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- Prediction of Return-to-original-work after an Industrial Accident Using Machine Learning and Comparison of Techniques
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Lee J, Kim HR
- KMID: 2410529
- J Korean Med Sci.
- 2018 May;33(19):e144.
- doi: 10.3346/jkms.2018.33.e144
BACKGROUND: Many studies have tried to develop predictors for return-to-work (RTW). However, since complex factors have been demonstrated to predict RTW, it is difficult to use them practically. This study... -
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- Machine Learning Applications in Endocrinology and Metabolism Research: An Overview
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Hong N, Park H, Rhee Y
- KMID: 2471722
- Endocrinol Metab.
- 2020 Mar;35(1):71-84.
- doi: 10.3803/EnM.2020.35.1.71
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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- Stacking Ensemble Technique for Classifying Breast Cancer
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Kwon H, Park J, Lee Y
- KMID: 2462226
- Healthc Inform Res.
- 2019 Oct;25(4):283-288.
- doi: 10.4258/hir.2019.25.4.283
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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