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

Natural Language Processing (650) Electronic Health Records (971) Machine Learning (1400) Artificial Intelligence (1214)

Published on in Vol 7, No 1 (2019): Jan-Mar

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/10788, first published .
Detection of Bleeding Events in Electronic Health Record Notes Using Convolutional Neural Network Models Enhanced With Recurrent Neural Network Autoencoders: Deep Learning Approach

Detection of Bleeding Events in Electronic Health Record Notes Using Convolutional Neural Network Models Enhanced With Recurrent Neural Network Autoencoders: Deep Learning Approach

Detection of Bleeding Events in Electronic Health Record Notes Using Convolutional Neural Network Models Enhanced With Recurrent Neural Network Autoencoders: Deep Learning Approach

Authors of this article:

Rumeng Li1 Author Orcid Image ;   Baotian Hu2 Author Orcid Image ;   Feifan Liu3 Author Orcid Image ;   Weisong Liu2 Author Orcid Image ;   Francesca Cunningham4 Author Orcid Image ;   David D McManus3, 5 Author Orcid Image ;   Hong Yu1, 2, 3, 6 Author Orcid Image
Article Authors Cited by (22) Tweetations (7) Metrics

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  2. Jang R, Kim N, Jang M, Lee K, Lee S, Lee K, Noh H, Seo J. Assessment of the Robustness of Convolutional Neural Networks in Labeling Noise by Using Chest X-Ray Images From Multiple Centers. JMIR Medical Informatics 2020;8(8):e18089 View
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  7. Sarker A, Al-Garadi M, Yang Y, Choi J, Quyyumi A, Martin G. Defining Patient-Oriented Natural Language Processing: A New Paradigm for Research and Development to Facilitate Adoption and Use by Medical Experts. JMIR Medical Informatics 2021;9(9):e18471 View
  8. Aronson J. Artificial Intelligence in Pharmacovigilance: An Introduction to Terms, Concepts, Applications, and Limitations. Drug Safety 2022;45(5):407 View
  9. Simmelink A, Gichuki C, Ampt F, Manguro G, Lim M, Agius P, Hellard M, Jaoko W, Stoové M, L'Engle K, Temmerman M, Gichangi P, Luchters S. Assessment of the lifetime prevalence and incidence of induced abortion and correlates among female sex workers in Mombasa, Kenya: a secondary cohort analysis. BMJ Open 2022;12(10):e053218 View
  10. Liu F, Zhou P, Baccei S, Masciocchi M, Amornsiripanitch N, Kiefe C, Rosen M. Qualifying Certainty in Radiology Reports through Deep Learning–Based Natural Language Processing. American Journal of Neuroradiology 2021 View
  11. Hann A, Meining A. Artificial Intelligence in Endoscopy. Visceral Medicine 2021;37(6):471 View
  12. Thapa R, Garikipati A, Shokouhi S, Hurtado M, Barnes G, Hoffman J, Calvert J, Katzmann L, Mao Q, Das R. Predicting Falls in Long-term Care Facilities: Machine Learning Study. JMIR Aging 2022;5(2):e35373 View
  13. Zhou Y, Chen W. Recurrent autoencoder model for unsupervised seismic facies analysis. Interpretation 2022;10(3):T451 View
  14. Gharagozloo M, Amrani A, Wittingstall K, Hamilton-Wright A, Gris D. Machine Learning in Modeling of Mouse Behavior. Frontiers in Neuroscience 2021;15 View
  15. Qureshi R, Irfan M, Ali H, Khan A, Nittala A, Ali S, Shah A, Gondal T, Sadak F, Shah Z, Hadi M, Khan S, Al-Tashi Q, Wu J, Bermak A, Alam T. Artificial Intelligence and Biosensors in Healthcare and Its Clinical Relevance: A Review. IEEE Access 2023;11:61600 View
  16. Laursen M, Pedersen J, Hansen R, Savarimuthu T, Lynggaard R, Vinholt P. Doctors Identify Hemorrhage Better during Chart Review when Assisted by Artificial Intelligence. Applied Clinical Informatics 2023;14(04):743 View

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