1 |
Becker's Health IT. Top 10 Countries for EHR Adoption, 2013. Available online at: https://www.beckershospitalreview.com/healthcare-information-technology/top-10-countries-for-ehr-adoption.html
|
2 |
The Health Institute for E-Health Policy. A Glimpse at EHR Implementation Around the World: The Lessons the US Can Learn, 2014. Available online at: https://www.e-healthpolicy.org/sites/e-healthpolicy.org/files/
|
3 |
S. M. Meystre, G. K. Savova, K. C. Kipper-Schuler, J. F. Hurdle, "Extracting information from textual documents in the electronic health record: a review of recent research", Yearbook of Medical Informatics, Vol. 17, No. 1, pp. 128-172, 2008. http://dx.doi.org/10.1055/s-0038-1638592
DOI
|
4 |
A. T. Azar, S. M. El-Metwally, "Decision tree classifiers for automated medical diagnosis", Neural Computing and Applications, Vol. 23, No. 2, pp. 2387-2403, 2012. http://dx.doi.org/10.1007/s00521-012-1196-7
DOI
|
5 |
A. Esteva, B. Kuprel, R. A. Novoa, J. Ko, S. M. Swetter, H. M. Blau, S. Thrun, "Dermatologist-level classification of skin cancer with deep neural networks", Nature, Vol. 542, No. 7639, pp. 115-118, 2017. http://dx.doi.org/10.1038/nature21056
DOI
|
6 |
B. A. H. I. Kaggle., "Kaggle Data Science Bowl", 2017.[Online]. Available: https://www.kaggle.com/c/data-science-bowl-2017.
|
7 |
F. Wang, R. Kaushal, D. Khullar, "Should health care demand interpretable artificial intelligence or accept "black box" medicine?", Annals of Internal Medicine, Vol. 172, pp. 59-60. 2020. http://dx.doi.org/10.7326/M19-2548
DOI
|
8 |
P. Rajpurkar, J. Irvin, K. Zhu, B. Yang, H. Mehta, T. Duan, D. Ding, A. Bagul, C. Langlotz, K. Shpanskaya, M. P. Lungren, A. Y. Ng, "CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning", 2017, https://doi.org/10.48550/arXiv.1711.05225
DOI
|
9 |
S. B. Lee, H. J. Lee, V. R. Singh, "Determining the Degree of Malignancy on Digital Mammograms by Artificial Intelligence Deep Learning", ScholarGen Publishers, Vol. 3, No. 1, pp. 17-32, 2020. https://doi.org/10.31916/SJMI2020-01-03
DOI
|
10 |
T. M. Noguerol, F. Paulano-Godino, M. T. Martin-Valdivia, C. O. Menias, A. Luna, "Strengths, Weaknesses, Opportunities, and Threats Analysis of Artificial Intelligence and Machine Learning Applications in Radiology", Journal of The American College of Radiology, Vol. 16, No. 9, pp. 1239-1247, 2019. http://dx.doi.org/10.1016/j.jacr.2019.05.047
DOI
|
11 |
Office of the National Coordinator for Health Information Technology. Non-federal Acute Care Hospital Electronic Health Record Adoption, Health IT Quick-Stat #47 2017. Available online at: https://www.healthit.gov/.
|
12 |
A. Singha, R. S. Thakur, T. Patel, "Deep Learning Applications in Medical Image Analysis", Biomedical Data Mining for Information Retrieval: Methodologies, Techniques and Applications, pp. 293-350, 2021. https://doi.org/10.1002/9781119711278.ch11
DOI
|
13 |
H. D. Yeo, H. K. Kan, "A Case Study on the Effect of the Artificial Intelligence Storytelling(AI+ST) Learning Method", Journal of The Korean Association of Information Education, Vol. 24, No. 5, pp. 495-509, 2020. http://doi.org/10.14352/jkaie.2020.24.5.495
DOI
|
14 |
A. Alansary, K. Kamnitsas, A. Davidson, R. Khlebnikov, M. Rajchl, C. Malamateniou, M. Rutherford, J. V. Hajnal, B. Glocker, D. Rueckert, B. Kainz,, "Fast Fully Automatic Segmentation of the Human Placenta from Motion Corrupted MRI", International Conference on Medical Image Computing and Computer-Assisted Intervention, Vol. 9901, pp. 589-597, 2016. https://doi.org/10.1007/978-3-319-46723-8_68
DOI
|
15 |
Y. Anavi, I. Kogan, E. Gelbart, O. Geva, H. Greenspan, "Visualizing and enhancing a deep learning framework using patients age and gender for chest X-ray image retrieval", Medical Imaging, Vol. 9785, pp. 978510, 2016. https://doi.org/10.1117/12.2217587
DOI
|
16 |
E. Shenkman, M. Hurt, W. Hogan, O. Carrasquillo, S. Smith, A. Brickman, D. Nelson, "OneFlorida Clinical Research Consortium: Linking a Clinical and Translational Science Institute With a Community-Based Distributive Medical Education Model", Academic Medicine (Ovid), Vol. 93, No. 3, pp. 451-455, 2018. http://dx.doi.org/10.1097/ACM.0000000000002029
DOI
|
17 |
S. B. Lo, S. A. Lou, J. S. Lin, M. T. Freedman, M. V. Chien, S. K. Mun, "Artificial convolution neural network techniques and applications for lung nodule detection", IEEE Transactions on Medical Imaging, Vol. 14, No. 4, pp. 711-718, 1995. https://doi.org/10.1109/42.476112
DOI
|
18 |
K. Kuan, M. Ravaut, G. Manek, H. Chen, J. Lin, B. Nazir, C. Chen, Tse C. Howe, Z. Zeng, V. Chandrasekhar, "Deep Learning for Lung Cancer Detection: Tackling the Kaggle Data Science Bowl 2017 Challenge", eprint arXiv:1705.09435, 2017. http://dx.doi.org/10.48550/arXiv.1705.09435
DOI
|
19 |
M. Prosperi, J. S. Min, J. Bian, F. Modave, "Big data hurdles in precision medicine and precision public health", BMC Medical Informatics and Decision Making, Vol. 18, pp. 139, 2018. http://dx.doi.org/10.1186/s12911-018-0719-2
DOI
|
20 |
https://www.healthmeasures.net/explore-measurement-systems/promis
|
21 |
Christian Leibig, Vaneeda Allken, Murat Seckin Ayhan, Philipp Berens, Siegfried Wahl, "Leveraging Uncertainty Information from Deep Neural Networks for Disease Detection", Scientific Report, Vol. 7, pp. 1-14, 2017. http://dx.doi.org/10.1038/s41598-017-17876-z
DOI
|
22 |
B. M. Wilamowski, "Neural network architectures and learning", IEEE International Conference on Industrial Technology, Vol. 1, pp. TU1-T12, 2003. https://doi.org/10.1109/ICIT.2003.1290197
DOI
|
23 |
B. V. Calster, L. Wynants, "Machine Learning in Medicine", The New England Journal of Medicine, Vol. 380, pp. 1347-1358, 2019. https://doi.org/10.1056/nejmc1906060
DOI
|
24 |
Ehsan Hosseini-Asl, Mohammed Ghazal, Ali Mahmoud, Ali Aslantas, Ahmed M Shalaby, Manual F Casanova, Gregory N Barnes, Georgy Gimel'farb, Robert Keynton, Ayman El-Baz, "Alzheimer's disease diagnostics by a 3D deeply supervised adaptable convolutional network", Frontiers in Bioscience (Landmark Edition), Vol. 23, No. 2, pp. 584-596, 2018. http://dx.doi.org/10.2741/4606
DOI
|
25 |
H. C. Shin, M. R. Orton, D. J. Collins, S. J. Doran, M. O. Leach, "Stacked Autoencoders for Unsupervised Feature Learning and Multiple Organ Detection in a Pilot Study Using 4D Patient DataPDF", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 35, No. 8, pp. 1930-1943, 2013. https://doi.org/10.1109/TPAMI.2012.277
DOI
|
26 |
S. Albarqouni, C. Baur, F. Achilles, V. Belagiannis, S. Demirci, N. Navab, "AggNet: Deep learning from crowds for mitosis detection in breast cancer histology images", IEEE transactions on medical imagin, Vol. 35, Vol. 5, pp. 1313-1321, 2016. https://doi.org/10.1109/tmi.2016.2528120
DOI
|
27 |
C. Giordano, M. Brennan, B. Mohamed, P. Rashidi, F. Modave, P. Tighe, "Accessing Artificial Intelligence for Clinical Decision-Making", Frontiers in Digital Health, Vol. 3, pp. 645232, 2021. https://doi.org/10.3389/fdgth.2021.645232
DOI
|
28 |
W. Shen, M. Zhou, F. Yang, C. Yang, J. Tian, "Multi-scale Convolutional Neural Networks for Lung Nodule Classification", Information Processing in Medical Imaging, Vol. 24. pp. 588-599, 2015. https://doi.org/10.1007/978-3-319-19992-4_46
DOI
|
29 |
Y. H. Lee, K. J. Kim, S. I. Lee, D. J. Kim, "Seq2Seq model-based Prognostics and Health Management of Robot Arm", The Journal of Korea Institute of Information, Electronics, and Communication Technology, Vol. 12, No. 3, pp. 242-250, 2019. https://doi.org/10.17661/jkiiect.2019.12.3.242
DOI
|
30 |
Xu Chen, Yue Zhao, Chuancai Liu, "Medical image segmentation using scalable functional variational Bayesian neural networks with Gaussian processes", Neurocomputing, Vol. 500, pp. 58-72, 2022. http://dx.doi.org/https://doi.org/10.1016/j.neucom.2022.05.055
DOI
|
31 |
B. E. Shon, S. M. Jeong, "Intelligent Hospital Information System Model for Medical AI Research/Development and Practical Use", Journal of The Korea Convergence Society, Vol. 13, No. 3, pp. 67-75, 2022. http://dx.doi.org/10.15207/JKCS.2022.13.03.067
DOI
|
32 |
https://ko.d2l.ai/chapter_deep-learning-basics/underfit-overfit.html
|
33 |
A. Rajkomar, S. Lingam, A. G. Taylor, M. Blum, J. Mongan, "High-Throughput Classification of Radiographs Using Deep Convolutional Neural Networks", Journal of Digital Imaging, Vol. 30, No. 1, pp. 95-101, 2017. http://dx.doi.org/10.1007/s10278-016-9914-9
DOI
|
34 |
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, A. Rabinovich, "Going deeper with convolutions", 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1-9, 2015. https://doi.org/10.1109/CVPR.2015.7298594
DOI
|
35 |
R. Li, W. Zhang, H. I. Suk, L. Wang, J. Li, D. Shen, S. Ji, "Deep learning based imaging data completion for improved brain disease diagnosis", Medical image computing and computer-assisted intervention, Vol. 17, pp. 305-312, 2014. https://doi.org/10.1007/978-3-319-10443-0_39
DOI
|
36 |
Z. Yan, Y. Zhan, Z. Peng, S. Liao, Y. Shinagawa, D. N. Metaxas, X. S. Zhou, "Bodypart Recognition Using Multi-stage Deep Learning", International Conference on Information Processing in Medical Imaging, Vol. 24. pp. 449-461, 2015. https://doi.org/10.1007/978-3-319-19992-4_35
DOI
|