Artificial Intelligence in Healthcare

AI, Machine Learning, and Deep and Intelligent Medicine Simplified for Everyone

Academic Edition, Chapter 10 - References/Further Reading

 

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  2. Yim J, Chopra R, Spitz T, Winkens J, Obika A, Kelly C, et al. Predicting conversion to wet age-related macular degeneration using deep learning. Nature Medicine. 2020 Jun;26(6):892–9.
  3. Wu G, Yang P, Xie Y, Woodruff HC, Rao X, Guiot J, et al. Development of a clinical decision support system for severity risk prediction and triage of COVID-19 patients at hospital admission: an international multicentre study. Eur Respir J. 2020;56(2).
  4. Gao Y, Cai G-Y, Fang W, Li H-Y, Wang S-Y, Chen L, et al. Machine learning based early warning system enables accurate mortality risk prediction for COVID-19. Nat Commun. 2020 06;11(1):5033.
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  6. Chen J, Chokshi S, Hegde R, Gonzalez J, Iturrate E, Aphinyanaphongs Y, et al. Development, Implementation, and Evaluation of a Personalized Machine Learning Algorithm for Clinical Decision Support: Case Study With Shingles Vaccination. J Med Internet Res. 2020 29;22(4):e16848.
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  8. Yoo TK, Ryu IH, Choi H, Kim JK, Lee IS, Kim JS, et al. Explainable Machine Learning Approach as a Tool to Understand Factors Used to Select the Refractive Surgery Technique on the Expert Level. Transl Vis Sci Technol [Internet]. [cited 2020 Nov 11];9(2). Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7346876/
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  10. Baghdadi A, Hoshyarmanesh H, de Lotbiniere-Bassett MP, Choi SK, Lama S, Sutherland GR. Data analytics interrogates robotic surgical performance using a microsurgery-specific haptic device. Expert Rev Med Devices. 2020 Jul;17(7):721–30.
  11. Fard MJ, Ameri S, Darin Ellis R, Chinnam RB, Pandya AK, Klein MD. Automated robot-assisted surgical skill evaluation: Predictive analytics approach. Int J Med Robot. 2018 Feb;14(1).
  12. Shih H, Rajendran S. Comparison of Time Series Methods and Machine Learning Algorithms for Forecasting Taiwan Blood Services Foundation’s Blood Supply. J Healthc Eng. 2019;2019:6123745.
  13. Srinivas S. A Machine Learning-Based Approach for Predicting Patient Punctuality in Ambulatory Care Centers. Int J Environ Res Public Health. 2020 24;17(10).
  14. Bressem KK, Adams LC, Gaudin RA, Tröltzsch D, Hamm B, Makowski MR, et al. Highly accurate classification of chest radiographic reports using a deep learning natural language model pretrained on 3.8 million text reports. Bioinformatics. 2020 Jul 23;
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  17. Shahid N, Rappon T, Berta W. Applications of artificial neural networks in health care organizational decision-making: A scoping review. PLOS ONE. 2019 Feb 19;14(2):e0212356.
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  19. Team TA. Main Types of Neural Networks and its Applications — Tutorial [Internet]. Medium. 2020 [cited 2020 Nov 11]. Available from: https://medium.com/towards-artificial-intelligence/main-types-of-neural-networks-and-its-applications-tutorial-734480d7ec8e
  20. Xu S, Wang Z, Sun J, Zhang Z, Wu Z, Yang T, et al. Using a deep recurrent neural network with EEG signal to detect Parkinson’s disease. Ann Transl Med [Internet]. 2020 Jul [cited 2020 Nov 11];8(14). Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7396761/
  21. Pathan RK, Biswas M, Khandaker MU. Time series prediction of COVID-19 by mutation rate analysis using recurrent neural network-based LSTM model. Chaos Solitons Fractals. 2020 Sep;138:110018.
  22. Lin E, Lin C-H, Lane H-Y. Relevant Applications of Generative Adversarial Networks in Drug Design and Discovery: Molecular De Novo Design, Dimensionality Reduction, and De Novo Peptide and Protein Design. Molecules [Internet]. 2020 Jul 16 [cited 2020 Nov 11];25(14). Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7397124/
  23. Jin L, Tan F, Jiang S. Generative Adversarial Network Technologies and Applications in Computer Vision. Comput Intell Neurosci [Internet]. 2020 Aug 1 [cited 2020 Nov 11];2020. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7416236/
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  31. Abdollahi B, Tomita N, Hassanpour S. Data Augmentation in Training Deep Learning Models for Medical Image Analysis. In: Nanni L, Brahnam S, Brattin R, Ghidoni S, Jain LC, editors. Deep Learners and Deep Learner Descriptors for Medical Applications [Internet]. Cham: Springer International Publishing; 2020 [cited 2020 Nov 11]. p. 167–80. (Intelligent Systems Reference Library). Available from: https://doi.org/10.1007/978-3-030-42750-4_6
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