Published on in Vol 22, No 9 (2020): September

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/20645, first published .
Marrying Medical Domain Knowledge With Deep Learning on Electronic Health Records: A Deep Visual Analytics Approach

Marrying Medical Domain Knowledge With Deep Learning on Electronic Health Records: A Deep Visual Analytics Approach

Marrying Medical Domain Knowledge With Deep Learning on Electronic Health Records: A Deep Visual Analytics Approach

Journals

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  18. Petmezas G, Papageorgiou V, Vassilikos V, Pagourelias E, Tsaklidis G, Katsaggelos A, Maglaveras N. Recent advancements and applications of deep learning in heart failure: Α systematic review. Computers in Biology and Medicine 2024;176:108557 View
  19. Li Z, Liu X, Tang Z, Jin N, Zhang P, Eadon M, Song Q, Chen Y, Su J. TrajVis: a visual clinical decision support system to translate artificial intelligence trajectory models in the precision management of chronic kidney disease. Journal of the American Medical Informatics Association 2024;31(11):2474 View
  20. Salih A, Galazzo I, Gkontra P, Rauseo E, Lee A, Lekadir K, Radeva P, Petersen S, Menegaz G. A review of evaluation approaches for explainable AI with applications in cardiology. Artificial Intelligence Review 2024;57(9) View
  21. Gao W, Rong F, Shao L, Deng Z, Xiao D, Zhang R, Chen C, Gong Z, Niu Z, Li F, Wei W, Ma L. Enhancing ophthalmology medical record management with multi-modal knowledge graphs. Scientific Reports 2024;14(1) View
  22. Horton A. Causal Economic Machine Learning (CEML): “Human AI”. AI 2024;5(4):1893 View
  23. ŞAHiN E, Arslan N, Özdemir D. Unlocking the black box: an in-depth review on interpretability, explainability, and reliability in deep learning. Neural Computing and Applications 2024 View