최소 단어 이상 선택하여야 합니다.
최대 10 단어까지만 선택 가능합니다.
다음과 같은 기능을 한번의 로그인으로 사용 할 수 있습니다.
NTIS 바로가기Industry promotion research = 산업진흥연구, v.7 no.1, 2022년, pp.1 - 8
We investigated various types of machine learning methods that can be applied to censored data. Exploratory data analysis reveals the distribution of each feature, relationships among features. Next, classification problem has been set up where the dependent variable is death_event while the rest of...
Chung, C., Schmidt, P. and Witte, A. (1991). Survival analysis: A survey. Journal of Quantitative Criminology, 7(1), 59-98.
Kleinbaum, D. G. and Klein, M. (2006). Survival analysis: A self-learning text. Springer Science & Business Media.
Wang, P., Li, Y. and Reddy, C. K. (2019). Machine learning for survival analysis: A Survey. ACM computing Surveys, 51(6), 1-36.
Cruz, J. A. and Wishart, D. S. (2006). Applications of machine learning in cancer prediction and prognosis. Cancer informatics, 2.
Kourou K. et al. (2015). Machine learning applications in cancer prognosis and prediction. Computational and Structural Biotechnology Journal, 13, 8-17.
WHO. (2016). Fact sheet on CVDs. Gloval Hearts. World Health Organization.
Ahmad, T. et al. (2017). Survival analysis of heart failure patients: A case study. PloS ONE, 12(7).
Chicco, D and Jurman, G. (2020). Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone. BMC Medical Informatics and Decision Making 20(16).
Al'Aref S. J. et al. (2019). Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging. European Heart Journal, 40(24), 1975-1986.
Dunn W. B. et al. (2007). Serum metabolomics reveals many novel metabolic markers of heart failure, including pseudouridine and 2-oxoglutarate. Metabolomics, 3(4), 413-426.
Ambale-Venkatesh B. et al. (2017). Cardiovascular event prediction by machine learning: the multi-ethnic study of atherosclerosis. Circr Res., 121(9), 1092-1101.
Panahiazar M. et al. (2015). Using EHRs and machine learning for heart failure survival analysis. Stud Health Technol Informat., 216, 40-44.
Ahmad T. et al. (2018). Machine learning methods improve prognostication, identify clinically distinct phenotypes, and detect heterogeneity in response to therapy in a large cohort of heart failure patients. Journal of American Heart Association, 7(8).
Krittanawong C. et al. (2019). Deep learning for cardiovascular medicine: A practical primer. European Heart Journal, 40, 2058-2073.
Bello G. A. et al. (2019). Deep learning cardiac motion analysis for human survival prediction. Nature Machine Intelligence, 1, 95-104.
Ishwaran, H. et al. (2008). Random Survival Forests. The Annuals of Applied Statistics, 2, 841-860.
*원문 PDF 파일 및 링크정보가 존재하지 않을 경우 KISTI DDS 시스템에서 제공하는 원문복사서비스를 사용할 수 있습니다.
Free Access. 출판사/학술단체 등이 허락한 무료 공개 사이트를 통해 자유로운 이용이 가능한 논문
※ AI-Helper는 부적절한 답변을 할 수 있습니다.