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NTIS 바로가기전자통신동향분석 = Electronics and telecommunications trends, v.34 no.1, 2019년, pp.98 - 110
우영춘 (IDX 원천기술연구실) , 이성엽 (IDX 원천기술연구실) , 최완 (IDX 원천기술연구실) , 안창원 (IDX 원천기술연구실) , 백옥기 (IDX 원천기술연구실)
Machine learning has been applied to medical imaging and has shown an excellent recognition rate. Recently, there has been much interest in preventive medicine. If data are accessible, machine learning packages can be used easily in digital healthcare fields. However, it is necessary to prepare the ...
서경원 외, "스마트 헬스케어 의료기기 기술," 표준전략보고서, 식품의약품안전평가원, 2018. 8.
송영준, "4차 산업혁명과 디지털 헬스케어 정책," 주간기술동향, 2018. 2.
정성원, "Healthcare에서 빅데이터의 활용," 제 5회 임상연구 방법론 워크숍, 가톨릭의대의생명산업연구원, 서울, 2016. 11. 5, pp. 18-29.
IBM, "Bigdata in Healthcare: Tapping New Insight to Save Lives," IBM Big Data & Analytics Hub, 2014. https://www.ibmbigdatahub.com/infographic/big-data-healthcare-tapping-new-insight-save-lives
Wikipedia, "Machine Learning," https://en.wikipedia.org/wiki/Machine_learning
정일영, 구원모, "헬스케어생태계 구축을위한 데이터통합 방안," 동향과 이슈, 제46호, 2018. 1, pp. 1-38.
MIT Critical Data, Secondary Analysis of Electronic Health Records, Springer International Publishing: NY, USA, 2016.
G. Press, "Cleaning Big Data: Most Time-Consuming, Least Enjoyable Data Science Task, Survey Says," Forbe, Mar. 23, 2016.
S. Christa, V. Suma, and L. Maduri, "An Effective Data Preprocessing Technique for Improved Data Management in a Distributed Environment," ACCTHPCA, vol. 3, July 2012, pp. 25-29.
SAS, "Data Visualization Techniques: From Basics to Big Data with SAS(R) Visual Analytics," SAS White Paper, 2018
P. van der Laken, "Facet," Google, June 2017. https://github.com/PAIR-code/facets
WIlliam H. Wolberg (physician), University of Wisconsin Hospitals. Madison, Wisconsin, USA, Breast Cancer Wisconsin (Original) Data Set, https://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+(original)
Tutorials Point, "Seaborn," TutorialsPoint, 2017. https://www.tutorialspoint.com/seaborn/seaborn_tutorial.pdf
A. Bilogur, "Missingno: A Missing Data Visualization Suite," J. Open Source Softw., Feb. 27, 2018, doi: 10.21105/joss.00547
Continuum Analytics, "Blaze Documentation," 2018. https://blaze.readthedocs.io/en/latest/index.html
G. Csardi and T. Nepusz, igraph Reference Manual, Harvard University: Cambridge, MA, USA, 2013.
Wikipedia, "Feature Engineering," https://en.wikipedia.org/wiki/Feature_engineering
A. Zheng, Evaluating Machine Learning Models, O'reilly: Sebastopol, CA, USA, 2015.
Medcalc, "ROC Curve Analysis," https://www.medcalc.org/manual/roc-curves.php
F.Y. Osisanwo et al., "Supervised Machine Learning Algorithms: Classification and Comparison," Int. J. Comput. Trends Technol., vol. 48, no. 3, June 2017, pp. 128-138.
P. Harrington, Machine Learning in Action, Manning Publications Co.: Shelter Island, NY, USA, 2012, pp. 83-100.
L. Arnold et al., "An Introduction to Deep Learning," in Proc. Eur. Symp. Artif. Neural Netw., Bruges, Belgium, Apr. 27-29, 2011, pp. 477-488.
Wikipedia, "Random Forest," https://en.wikipedia.org/wiki/Random_forest
Wikipedia, "Boosting," https://en.wikipedia.org/wiki/Boosting_(machine_learning)
R.E. Schapire, "The Boosting Approach to Machine Learning, An Overview," in MSRI Workshop on Nonlinear Estimation and Classification, Springer: Heidelberg, Germany, 2002, pp. 3-4.
A. Natekin and A. Knoll, "Gradient Boosting Machines, a Tutorial," Front. Neurorobot., July 21, 2013, doi: 10.3389/fnbot.2013.00021.
G. Biau, B. Cadre, and L. Rouviere, "Accelerated Gradient Boosting," arXiv:1803.02042, May 2018.
J. Brownlee, "XGBoost with Python, Gradien Boosted Trees with XGBoost and Scikit-learn," Machine Learning Mastery, Sept. 19, 2016.
G. Ke et al., "LGBM LightGBM: A Highly Efficient Gradient Boosting Decision Tree," Conf. Neural Inform. Process. Syst., Long Beach, CA, USA, 2017, pp. 1-9.
A. Veronika, D.V. Ershov, and A. Guli, "CatBoost: Gradient Boosting with Categorical Features Support," Yandex, 2017. https://catboost.ai/
M. Du, N. Liu, and X. Hu, "Techniques for Interpretable Machine Learning," arXiv:1808.00033, July 2018.
M.T. Ribeiro, S. Singh, and C. Guestrin, "Why Should I Trust You?" Proc. ACM SIGKDD Int. Conf. Knowled. Discovery Data Mining, San Francisco, CA, USA, Aug. 13-17, 2016, pp. 1135-1144.
S.M. Lundberg and S.-I. Lee, "A Unified Approach to Interpreting Model Predictions," Conf. Neural Inform. Process. Syst., Long Beach, CA, USA, 2017, pp. 1-10.
A. Saabas, "treeinterpreter, 2015. https://github.com/andosa/treeinterpreter
D. Foster, "xgboostExplainer," 2017. https://github.com/AppliedDataSciencePartners/xgboostExplainer
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