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

Adverse Drug Events Detection, Pharmacovigilance and Surveillance (143) Clinical Information and Decision Making (1314) Natural Language Processing (636) Decision Support for Health Professionals (1127) Electronic Health Records (961) Pharmacovigilance (125) Machine Learning (1370)

Published on in Vol 6, No 4 (2018): Oct-Dec

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/12159, first published .
Extraction of Information Related to Adverse Drug Events from Electronic Health Record Notes: Design of an End-to-End Model Based on Deep Learning

Extraction of Information Related to Adverse Drug Events from Electronic Health Record Notes: Design of an End-to-End Model Based on Deep Learning

Extraction of Information Related to Adverse Drug Events from Electronic Health Record Notes: Design of an End-to-End Model Based on Deep Learning

Authors of this article:

Fei Li1, 2, 3 Author Orcid Image ;   Weisong Liu1, 2, 3 Author Orcid Image ;   Hong Yu1, 2, 3, 4 Author Orcid Image
Article Authors Cited by (38) Tweetations (9) Metrics

Journals

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  14. Mitra A, Rawat B, McManus D, Yu H. Relation Classification for Bleeding Events From Electronic Health Records Using Deep Learning Systems: An Empirical Study. JMIR Medical Informatics 2021;9(7):e27527 View
  15. Bright R, Rankin S, Dowdy K, Blok S, Bright S, Palmer L. Finding Potential Adverse Events in the Unstructured Text of Electronic Health Care Records: Development of the Shakespeare Method. JMIRx Med 2021;2(3):e27017 View
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  19. Wieder R, Adam N. Drug repositioning for cancer in the era of AI, big omics, and real-world data. Critical Reviews in Oncology/Hematology 2022;175:103730 View
  20. Gharagozloo M, Amrani A, Wittingstall K, Hamilton-Wright A, Gris D. Machine Learning in Modeling of Mouse Behavior. Frontiers in Neuroscience 2021;15 View
  21. Luo X, Gandhi P, Storey S, Huang K. A Deep Language Model for Symptom Extraction From Clinical Text and its Application to Extract COVID-19 Symptoms From Social Media. IEEE Journal of Biomedical and Health Informatics 2022;26(4):1737 View
  22. 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
  23. Trajanov D, Trajkovski V, Dimitrieva M, Dobreva J, Jovanovik M, Klemen M, Žagar A, Robnik-Šikonja M, Khoshbouei H. Review of Natural Language Processing in Pharmacology. Pharmacological Reviews 2023;75(4):714 View
  24. Deimazar G, Sheikhtaheri A. Machine learning models to detect and predict patient safety events using electronic health records: A systematic review. International Journal of Medical Informatics 2023;180:105246 View
  25. 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
  26. Fraile Navarro D, Ijaz K, Rezazadegan D, Rahimi-Ardabili H, Dras M, Coiera E, Berkovsky S. Clinical named entity recognition and relation extraction using natural language processing of medical free text: A systematic review. International Journal of Medical Informatics 2023;177:105122 View
  27. Li D, Ma H, Li W, Zhao B, Zhao J, Liu Y, Fu J. KTI-RNN: Recognition of Heart Failure from Clinical Notes. Tsinghua Science and Technology 2023;28(1):117 View
  28. Modi S, Kasmiran K, Mohd Sharef N, Sharum M. Extracting adverse drug events from clinical Notes: A systematic review of approaches used. Journal of Biomedical Informatics 2024;151:104603 View
  29. Li Y, Tao W, Li Z, Sun Z, Li F, Fenton S, Xu H, Tao C. Artificial intelligence-powered pharmacovigilance: A review of machine and deep learning in clinical text-based adverse drug event detection for benchmark datasets. Journal of Biomedical Informatics 2024;152:104621 View
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