과제정보
본 연구는 과학기술정보통신부 및 정보통신기획평가원의 인공지능융합혁신인재양성사업 연구 결과로 수행되었음(IITP-2023-RS-2023-00256629) 본 연구는 한국연구재단 연구과제로 수행되었습니다. (This work was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2022R1A2C1010364).)
참고문헌
- Kim, Sang Hyuk, et al. "Recent prevalence of and factors associated with chronic obstructive pulmonary disease in a rapidly aging society: Korea National Health and Nutrition Examination Survey 2015-2019." Journal of Korean Medical Science 38.14 (2023).
- 대한결핵 및 호흡기학회. "COPD 진료지침 2014 개정" (2014): 46-47.
- Spruit, Martijn A., et al. "Profiling of patients with COPD for adequate referral to exercise-based care: the Dutch model." Sports Medicine 50 (2020): 1421-1429. https://doi.org/10.1007/s40279-020-01286-9
- 이태헌, and 이남. "중증 COPD 환자에 대한 포괄적인 운동프로그램의 장기 효과-단일사례연구."대한심장호흡물리치료학회지 8.2 (2020): 1-9.
- Kingma, Diederik P., and Max Welling. "Auto-encoding variational bayes." arXiv preprint arXiv:1312.6114 (2013).
- Ishfaq, Haque, Assaf Hoogi, and Daniel Rubin. "TVAE: Triplet-based variational autoencoder using metric learning." arXiv preprint arXiv:1802.04403 (2018).
- Goodfellow, Ian, et al. "Generative adversarial nets." Advances in neural information processing systems 27 (2014).
- Xu, Lei, et al. "Modeling tabular data using conditional gan." Advances in neural information processing systems 32 (2019).
- 질병관리청 국민건강영양조사원시자료, https://knhanes.kdca.go.kr/knhanes/sub03/sub03_02_05.do
- 질병관리청. (2013). 2012 Korea national health and nutrition examination survey results. 서울, 대한민국: 보건복지부. https://knhanes.cdc.go.kr/knhanes/index.do
- Chen, Tianqi, and Carlos Guestrin. "Xgboost: A scalable tree boosting system." Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining.
- Patki, Neha, Roy Wedge, and Kalyan Veeramachaneni. "The synthetic data vault." 2016 IEEE international conference on data science and advanced analytics (DSAA). IEEE, 2016.