This paper is in the following e-collection/theme issue:

Artificial Intelligence (1371) Clinical Informatics (1022) Machine Learning (1465)

Published on in Vol 22, No 12 (2020): December

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/18418, first published .
Limitations of Deep Learning Attention Mechanisms in Clinical Research: Empirical Case Study Based on the Korean Diabetic Disease Setting

Limitations of Deep Learning Attention Mechanisms in Clinical Research: Empirical Case Study Based on the Korean Diabetic Disease Setting

Limitations of Deep Learning Attention Mechanisms in Clinical Research: Empirical Case Study Based on the Korean Diabetic Disease Setting

Authors of this article:

Junetae Kim1, 2, 3 Author Orcid Image ;   Sangwon Lee2 Author Orcid Image ;   Eugene Hwang4 Author Orcid Image ;   Kwang Sun Ryu2 Author Orcid Image ;   Hanseok Jeong1 Author Orcid Image ;   Jae Wook Lee5 Author Orcid Image ;   Yul Hwangbo3, 6 Author Orcid Image ;   Kui Son Choi1, 2 Author Orcid Image ;   Hyo Soung Cha1, 2 Author Orcid Image
Article Authors Cited by (15) Tweetations (6) Metrics

Journals

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  2. Tran T, Lee J, Gunathilake M, Kim J, Kim S, Cho H, Kim J. A comparison of machine learning models and Cox proportional hazards models regarding their ability to predict the risk of gastrointestinal cancer based on metabolic syndrome and its components. Frontiers in Oncology 2023;13 View
  3. Khan J, Khan J, Ali F, Ullah F, Bacha J, Lee S. Artificial Intelligence and Internet of Things (AI-IoT) Technologies in Response to COVID-19 Pandemic: A Systematic Review. IEEE Access 2022;10:62613 View
  4. Karatzia L, Aung N, Aksentijevic D. Artificial intelligence in cardiology: Hope for the future and power for the present. Frontiers in Cardiovascular Medicine 2022;9 View
  5. Zhou Z, Jafarzadeh Ghoushchi S. Detection and Counting Method of Pigs Based on YOLOV5_Plus: A Combination of YOLOV5 and Attention Mechanism. Mathematical Problems in Engineering 2022;2022:1 View
  6. Hwang E, Park H, Kim H, Kim J, Jeong H, Kim J, Kim S. Development of a Bispectral index score prediction model based on an interpretable deep learning algorithm. Artificial Intelligence in Medicine 2023;143:102569 View
  7. Baker S, Xiang W. Artificial Intelligence of Things for Smarter Healthcare: A Survey of Advancements, Challenges, and Opportunities. IEEE Communications Surveys & Tutorials 2023;25(2):1261 View
  8. Brasse J, Broder H, Förster M, Klier M, Sigler I. Explainable artificial intelligence in information systems: A review of the status quo and future research directions. Electronic Markets 2023;33(1) View
  9. Wang C, Zhou Y. Rethinking the role of attention mechanism: a causality perspective. Applied Intelligence 2024;54(2):1862 View
  10. Manh V, Jia X, Xue W, Xu W, Mei Z, Dong Y, Zhou J, Huang R, Ni D. An efficient framework for lesion segmentation in ultrasound images using global adversarial learning and region-invariant loss. Computers in Biology and Medicine 2024;171:108137 View
  11. Xie J, Wang Z, Yu Z, Ding Y, Guo B. Prototype Learning for Medical Time Series Classification via Human–Machine Collaboration. Sensors 2024;24(8):2655 View
  12. Gong K, Xue Y, Kong L, Xie X. Cost prediction for ischemic heart disease hospitalization: Interpretable feature extraction using network analysis. Journal of Biomedical Informatics 2024;154:104652 View
  13. 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

Books/Policy Documents

  1. Zheng J, Zhuang Z, Liao T, Chen L. Proceedings of the 12th International Conference on Computer Engineering and Networks. View
  2. Tiwari M, Waoo A. Revolutionizing Healthcare: AI Integration with IoT for Enhanced Patient Outcomes. View