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

Clinical Information and Decision Making (1328) Adverse Drug Events Detection, Pharmacovigilance and Surveillance (146) Pharmacovigilance (128) Machine Learning (1384)

Published on in Vol 21, No 2 (2019): February

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/11016, first published .
Detecting Potential Adverse Drug Reactions Using a Deep Neural Network Model

Detecting Potential Adverse Drug Reactions Using a Deep Neural Network Model

Detecting Potential Adverse Drug Reactions Using a Deep Neural Network Model

Authors of this article:

Chi-Shiang Wang1 Author Orcid Image ;   Pei-Ju Lin1 Author Orcid Image ;   Ching-Lan Cheng2, 3, 4 Author Orcid Image ;   Shu-Hua Tai4 Author Orcid Image ;   Yea-Huei Kao Yang2, 3 Author Orcid Image ;   Jung-Hsien Chiang1, 5 Author Orcid Image
Article Authors Cited by (70) Tweetations Metrics

Journals

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  25. Das P, Mazumder D. An extensive survey on the use of supervised machine learning techniques in the past two decades for prediction of drug side effects. Artificial Intelligence Review 2023;56(9):9809 View
  26. Chopard D, Treder M, Corcoran P, Ahmed N, Johnson C, Busse M, Spasic I. Text Mining of Adverse Events in Clinical Trials: Deep Learning Approach. JMIR Medical Informatics 2021;9(12):e28632 View
  27. Nguyen D, Nguyen C, Petschner P, Mamitsuka H. SPARSE: a sparse hypergraph neural network for learning multiple types of latent combinations to accurately predict drug–drug interactions. Bioinformatics 2022;38(Supplement_1):i333 View
  28. Huang J, Lee W, Lee K. Predicting Adverse Drug Reactions from Social Media Posts: Data Balance, Feature Selection and Deep Learning. Healthcare 2022;10(4):618 View
  29. Rodríguez-Rodríguez I, Rodríguez J, Shirvanizadeh N, Ortiz A, Pardo-Quiles D. Applications of Artificial Intelligence, Machine Learning, Big Data and the Internet of Things to the COVID-19 Pandemic: A Scientometric Review Using Text Mining. International Journal of Environmental Research and Public Health 2021;18(16):8578 View
  30. Falconer N, Barras M, Abdel-Hafiz A, Radburn S, Cottrell N. Evaluation of two European risk models for predicting medication harm in an Australian patient cohort. European Journal of Clinical Pharmacology 2022;78(4):679 View
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  33. Hlavaty A, Roustit M, Montani D, Chaumais M, Guignabert C, Humbert M, Cracowski J, Khouri C. Identifying new drugs associated with pulmonary arterial hypertension: A WHO pharmacovigilance database disproportionality analysis. British Journal of Clinical Pharmacology 2022;88(12):5227 View
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Books/Policy Documents

  1. Dabare R, Wong K, Shiratuddin M, Koutsakis P. Neural Information Processing. View
  2. Nova S, Rahman M, Hosen A. Rhythms in Healthcare. View
  3. Das P, Sangma J, Pal V, Yogita . Practical Applications of Computational Biology & Bioinformatics, 15th International Conference (PACBB 2021). View
  4. Dabare R, Wong K, Shiratuddin M, Koutsakis P. Neural Information Processing. View
  5. Piroozmand F, Mohammadipanah F, Sajedi H. A Handbook of Artificial Intelligence in Drug Delivery. View
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  7. Dey A, Shrivastava J, Kumar C. Intelligent Human Centered Computing. View
  8. Das P, Mazumder D. Mathematical Modeling and Intelligent Control for Combating Pandemics. View