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

Natural Language Processing (706) Machine Learning (1501) Adverse Drug Events Detection, Pharmacovigilance and Surveillance (159) Pharmacovigilance (138)

Published on in Vol 8, No 7 (2020): July

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/18417, first published .
Extraction of Information Related to Drug Safety Surveillance From Electronic Health Record Notes: Joint Modeling of Entities and Relations Using Knowledge-Aware Neural Attentive Models

Extraction of Information Related to Drug Safety Surveillance From Electronic Health Record Notes: Joint Modeling of Entities and Relations Using Knowledge-Aware Neural Attentive Models

Extraction of Information Related to Drug Safety Surveillance From Electronic Health Record Notes: Joint Modeling of Entities and Relations Using Knowledge-Aware Neural Attentive Models

Authors of this article:

Bharath Dandala1 Author Orcid Image ;   Venkata Joopudi1 Author Orcid Image ;   Ching-Huei Tsou1 Author Orcid Image ;   Jennifer J Liang1 Author Orcid Image ;   Parthasarathy Suryanarayanan1 Author Orcid Image
Article Authors Cited by (22) Tweetations (3) Metrics

Journals

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  2. Berthelot J, Darrieutort-Laffite C, Le Goff B. Utilité des suivis de cohortes de patients de la vraie-vie par questionnaires pour évaluer l’effet des traitements sur l’ensemble de la population. Revue du Rhumatisme 2022;89(2):107 View
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  9. Ramachandran G, Lybarger K, Liu Y, Mahajan D, Liang J, Tsou C, Yetisgen M, Uzuner Ö. Extracting medication changes in clinical narratives using pre-trained language models. Journal of Biomedical Informatics 2023;139:104302 View
  10. Wang Q, Li C. Evaluating risk propagation in renewable energy incidents using ontology-based Bayesian networks extracted from news reports. International Journal of Green Energy 2022;19(12):1290 View
  11. Timilsina M, Tandan M, Nováček V. Machine learning approaches for predicting the onset time of the adverse drug events in oncology. Machine Learning with Applications 2022;9:100367 View
  12. Breit A, Waltersdorfer L, Ekaputra F, Sabou M, Ekelhart A, Iana A, Paulheim H, Portisch J, Revenko A, Teije A, Van Harmelen F. Combining Machine Learning and Semantic Web: A Systematic Mapping Study. ACM Computing Surveys 2023;55(14s):1 View
  13. Timilsina M, Tandan M, Nováček V. Machine Learning Approaches for Predicting the Onset Time of the Adverse Drug Events in Oncology. SSRN Electronic Journal 2022 View
  14. Botsis T, Kreimeyer K. Improving drug safety with adverse event detection using natural language processing. Expert Opinion on Drug Safety 2023;22(8):659 View
  15. Cai L, Li J, Lv H, Liu W, Niu H, Wang Z. Integrating domain knowledge for biomedical text analysis into deep learning: A survey. Journal of Biomedical Informatics 2023;143:104418 View
  16. Herman Bernardim Andrade G, Nishiyama T, Fujimaki T, Yada S, Wakamiya S, Takagi M, Kato M, Miyashiro I, Aramaki E. Assessing domain adaptation in adverse drug event extraction on real-world breast cancer records. International Journal of Medical Informatics 2024;191:105539 View
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Books/Policy Documents

  1. Waltersdorfer L, Breit A, Ekaputra F, Sabou M. Database and Expert Systems Applications - DEXA 2021 Workshops. View
  2. Devarakonda M, Raja K, Xu H. Natural Language Processing in Biomedicine. View