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

Adverse Drug Events Detection, Pharmacovigilance and Surveillance (159) New Methods (963) Pharmacovigilance (138) Natural Language Processing (706) Clinical Informatics (1050) Safety and Error Prevention in Health (206) Big Data (245)
Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/26407, first published .
Chinese-Named Entity Recognition From Adverse Drug Event Records: Radical Embedding-Combined Dynamic Embedding–Based BERT in a Bidirectional Long Short-term Conditional Random Field (Bi-LSTM-CRF) Model

Chinese-Named Entity Recognition From Adverse Drug Event Records: Radical Embedding-Combined Dynamic Embedding–Based BERT in a Bidirectional Long Short-term Conditional Random Field (Bi-LSTM-CRF) Model

Chinese-Named Entity Recognition From Adverse Drug Event Records: Radical Embedding-Combined Dynamic Embedding–Based BERT in a Bidirectional Long Short-term Conditional Random Field (Bi-LSTM-CRF) Model

Authors of this article:

Hong Wu1 Author Orcid Image ;   Jiatong Ji2 Author Orcid Image ;   Haimei Tian3 Author Orcid Image ;   Yao Chen1 Author Orcid Image ;   Weihong Ge4 Author Orcid Image ;   Haixia Zhang4 Author Orcid Image ;   Feng Yu2 Author Orcid Image ;   Jianjun Zou5 Author Orcid Image ;   Mitsuhiro Nakamura6 Author Orcid Image ;   Jun Liao1 Author Orcid Image
Article Authors Cited by (11) Tweetations (3) Metrics

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  2. Feng Z, Wu X, Ma J, Li M, He G, Cao D, Yang G. DKADE: a novel framework based on deep learning and knowledge graph for identifying adverse drug events and related medications. Briefings in Bioinformatics 2023;24(4) View
  3. Chen Y, Li X, Li A, Li Y, Yang X, Lin Z, Yu S, Tang X. A Deep Learning Model for the Normalization of Institution Names by Multisource Literature Feature Fusion: Algorithm Development Study. JMIR Formative Research 2023;7:e47434 View
  4. Li Q, Jiang M. A representation learning model based on stochastic perturbation and homophily constraint. Knowledge and Information Systems 2023;65(12):5353 View
  5. Yang T, Sucholutsky I, Jen K, Schonlau M. exKidneyBERT: a language model for kidney transplant pathology reports and the crucial role of extended vocabularies. PeerJ Computer Science 2024;10:e1888 View
  6. 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
  7. Nishiyama T, Yamaguchi A, Han P, Pereira L, Otsuki Y, Andrade G, Kudo N, Yada S, Wakamiya S, Aramaki E, Takada M, Toi M. Automated System to Capture Patient Symptoms From Multitype Japanese Clinical Texts: Retrospective Study. JMIR Medical Informatics 2024;12:e58977 View
  8. Liu S, Wang A, Xiu X, Zhong M, Wu S. Evaluating Medical Entity Recognition in Health Care: Entity Model Quantitative Study. JMIR Medical Informatics 2024;12:e59782 View
  9. Kawazoe Y, Shimamoto K, Seki T, Tsuchiya M, Shinohara E, Yada S, Wakamiya S, Imai S, Hori S, Aramaki E. Post-marketing surveillance of anticancer drugs using natural language processing of electronic medical records. npj Digital Medicine 2024;7(1) View
  10. Tang J, Huang Z, Xu H, Zhang H, Huang H, Tang M, Luo P, Qin D. Chinese Clinical Named Entity Recognition With Segmentation Synonym Sentence Synthesis Mechanism: Algorithm Development and Validation. JMIR Medical Informatics 2024;12:e60334 View