다음과 같은 기능을 한번의 로그인으로 사용 할 수 있습니다.
NTIS 바로가기
한국융합학회논문지 = Journal of the Korea Convergence Society, v.13 no.1, 2022년, pp.141 - 147
박민서 (서울여자대학교 데이터사이언스학과)
AI-Helper
In a broad sense, the definition of digital health care is an industrial area that manages personal health and diseases through the convergence of the health care industry and ICT. In a narrow sense, various medical technologies are used to manage medical services to improve patient health. This pap...
G. Iyawa, M. Herselman & A. Botha (2016). Digital Health Innovation Ecosystems: From Systematic Literature Review to Conceptual Framework. In:Procedia Computer Science, 100, 244-252. DOI : 10.1016/J.PROCS.2016.09.149
A. Arora. (2020). Conceptualising Artificial Intelligence as a Digital Healthcare Innovation: An Introductory Review". In: Medical Devices: Evidence and Research, 13 223-230. DOI : 10.2147/MDER.S262590.
J. Hwang. (2021). Global Health Care Outlook , Deloitte Insights
C. Bishop. (2006). Pattern Recognition and Machine Learning (Information Science and Statistics). Berlin, Heidelberg: Springer-Verlag, ISBN: 0387310738.
T. Hastie, R. Tibshirani & J. Friedman. (2001). The Elements of Statistical Learning. Springer Series in Statistics,New York, NY, USA: Springer New York Inc.
R. Schapire & Y. Freund. (2012). Boosting: Foundations and Algorithms, The MIT Press, ISBN: 0262017180, 9780262017183.
K. Yu, A. Beam & I. Kohane (2018). Artificial intelligence in healthcare". In: Nature Biomedical Engineering, 2, 719-731.
Y. Woo, S. Lee, C. Choi, C. Ahn & O. Baek(2019). Trend of Utilization of Machine Learning Technology for Digital Healthcare Data Analysis, ETRI, 34.
A. Qayyum et al. (2020). Secure and Robust Machine Learning for Healthcare: A Survey. In: IEEE Reviews in Biomedical Engineering. DOI : 10.1109/RBME.2020.3013489.
B. Peter, L. Jensen & S. Brunak. (2012). "Mining electronic health records: towards better research applications and clinical care. In: Nature Reviews Genetics, 13, 395-405. DOI : 10.1038/nrg3208.
Z. Wang et al. (2012). Extracting Diagnoses and Investigation Results from Unstructured Text in Electronic Health Records by Semi-Supervised Machine Learning. In: PLOS ONE, 7(1), 1-9. DOI : 10.1371/journal.pone.0030412.
T. Zheng et al. (2017). A machine learning-based framework to identify type 2 diabetes through electronic health records. In: International journal of medical informatics, 97, 120-127. DOI : 10.1016/j.ijmedinf.2016.09.014.
B. Nestor et al. (2019). Feature Robustness in Non-stationary Health Records: Caveats to Deployable Model Performance in Common Clinical Machine Learning Tasks. arXiv: 1908.00690 [cs.LG].
E. Long et al. (2017). An artificial intelligence platform for the multihospital collaborative management of congenital cataracts. Nature biomedical engineering, 1(2), 1-8. DOI : 10.1038/s41551-016-0024.
A. Esteva et al. (2017). Dermatologist-level classification of skin cancer with deep neural networks. In: Nature, 542. DOI : 10.1038/nature21056.
V. Gulshan et al. (2016). Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. In: JAMA, 316. DOI : 10.1001/jama.2016.17216.
J. Zech et al. (2018). Natural Language-based Machine Learning Models for the Annotation of Clinical Radiology Reports. In: Radiology, 287, 570-580. DOI : 10.1148/radiol.2018171093.
B. Jing, P. Xie & E. Xing. (2018). "On the Automatic Generation of Medical Imaging Reports". In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, 1. DOI : 10.18653/v1/p18-1240
X. Wang et al. (2018). TieNet: Text-Image Embedding Networkfor Common Thorax Disease Classification and Reporting in Chest X-rays. arXiv: 1801.04334 [cs.CV].
Y. Xue et al. (2018). Multimodal Recurrent Model with Attention for Automated Radiology Report Generation. 21st International Conference, Granada, Spain, September 16-20, Proceedings, Part I, (pp. 457-466). DOI : 10.1007/978-3-030-00928-1_52.
V. Jindal. (2016). Integrating Mobile and Cloud for PPG Signal Selection to Monitor Heart Rate during Intensive Physical Exercise. In: 2016 IEEE/ACM International Conference on Mobile Software Engineering and Systems (MOBILESoft), (pp. 36-37). DOI : 10.1109/MobileSoft.2016.027.
F. Attal et al. (2015). Physical Human Activity Recognition Using Wearable Sensors. In: Sensors, 15, pp. 31314-31338, DOI : 10.3390/s151229858.
J. Cruz & D. Wishart. (2007). Applications of Machine Learning in Cancer Prediction and Prognosis. In: Cancer Informatics, 2, (pp. 59-77). DOI : 10.1177/117693510600200030
S. Weng et al. (2017). Can Machine-learning improve cardiovascular risk prediction using routine clinical data?. In: PLoS ONE, 12, DOI : 10.1371/journal.pone.0174944
M. Fatima & M. Pasha. (2017). Survey of Machine Learning Algorithms for Disease Diagnostic. In: Journal of Intelligent Learning Systems and Applications, 09, (pp. 1-16). DOI : 10.4236/jilsa.2017.91001.
https://www.berghealth.com/
https://www.p1vital.com/
A. Collins & Y. Yao. (2018). Machine Learning Approaches: Data Integration for Disease Prediction and Prognosis. In Applied Computational Genomics (pp. 137-141). Springer, Singapore. DOI : 10.1007/978-981-13-1071-3_10.
P. Afshar, A. Mohammadi & K. Plataniotis. (2018). Brain Tumor Type Classification via Capsule Networks. In 2018 25th IEEE International Conference on Image Processing (ICIP) (pp. 3129-3133). IEEE. DOI : 10.1109/ICIP.2018.8451379.
W. Zhu et al. (2018). DeepLung: Deep 3D Dual Path Nets for Automated Pulmonary Nodule Detection and Classification. In 2018 IEEE Winter Conference on Applications of Computer Vision (WACV) (pp. 673-681). arXiv: 1801.09555 [cs.CV].
H. Novatchkov & A. Baca. (2013). Artificial intelligence in sports on the example of weight training. J Sports Sci Med, 12(1), 27-37.
L. Jia & L. Li. (2020). Research on core strength training of aerobics based on artificial intelligence and sensor network. J Wireless Com Network, 164.
E. Pinero-Fuentes et al. (2021). A Deep-Learning Based Posture Detection System for Preventing Telework-Related Musculoskeletal Disorders. Sensors, 21, 5236. DOI : 10.3390/s21155236
A. Aguirre et al. (2021). Machine Learning Approach for Fatigue Estimation in sit to stand exercise. Sensors, 21, 5006. DOI : 10.3390/s21155006
F. Galbusera, G. Casaroli & T. Bassani. (2019). Artificial intelligence and machine learning in spine research. JOR Spine. DOI : 10.1002/jsp2.1044
Z. Shen, A. Shehzad, S. Chen, H. Sun & J. Liu (2020). "Machine Learning Based Approach on Food Recognition and Nutrition Estimation. Procedia Computer Science, 174, 448-453. DOI:10.1016/j.procs.2020.06.113
P. Pouladzadeh, G. Villalobos, R. Almaghrabi & S. Shirmohammadi (2012). A Novel SVM Based Food Recognition Method for Calorie Measurement Applications. IEEE International Conference on Multimedia and Expo Workshops, (pp. 495-498). DOI : 10.1109/ICMEW.2012.92.
'Deep learning technology to manage diet smartly.' https://tech.kakaoenterprise.com/84
N. Jhamat, G. Mustafa, Z. Arshad & R. Abbas. (2021). Expert System for Recommendations of Healthy Food Recipes using machine learning. xIlkogretim Online - Elementary Education Online, 20(5), 2867-2874. DOI : 10.17051/ilkonline.2021.05.313
*원문 PDF 파일 및 링크정보가 존재하지 않을 경우 KISTI DDS 시스템에서 제공하는 원문복사서비스를 사용할 수 있습니다.
출판사/학술단체 등이 한시적으로 특별한 프로모션 또는 일정기간 경과 후 접근을 허용하여, 출판사/학술단체 등의 사이트에서 이용 가능한 논문
Copyright KISTI. All Rights Reserved.
※ AI-Helper는 부적절한 답변을 할 수 있습니다.