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

Clinical Information and Decision Making (1042) Adverse Drug Events Detection, Pharmacovigilance and Surveillance (104) Pharmacovigilance (93) Machine Learning (1121)

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 Wang 1 Author Orcid Image ;   Pei-Ju Lin 1 Author Orcid Image ;   Ching-Lan Cheng 2, 3, 4 Author Orcid Image ;   Shu-Hua Tai 4 Author Orcid Image ;   Yea-Huei Kao Yang 2, 3 Author Orcid Image ;   Jung-Hsien Chiang 1, 5 Author Orcid Image
Article Authors Cited by (48) Tweetations Metrics

Journals

  1. Li X, Lin X, Ren H, Guo J. Ontological Organization and Bioinformatic Analysis of Adverse Drug Reactions From Package Inserts: Development and Usability Study. Journal of Medical Internet Research 2020;22(7):e20443 View
  2. Tobore I, Li J, Yuhang L, Al-Handarish Y, Kandwal A, Nie Z, Wang L. Deep Learning Intervention for Health Care Challenges: Some Biomedical Domain Considerations. JMIR mHealth and uHealth 2019;7(8):e11966 View
  3. Timilsina M, Tandan M, d’Aquin M, Yang H. Discovering Links Between Side Effects and Drugs Using a Diffusion Based Method. Scientific Reports 2019;9(1) View
  4. Rohani N, Eslahchi C. Drug-Drug Interaction Predicting by Neural Network Using Integrated Similarity. Scientific Reports 2019;9(1) View
  5. Pérez-Parras Toledano J, García-Pedrajas N, Cerruela-García G. Multilabel and Missing Label Methods for Binary Quantitative Structure–Activity Relationship Models: An Application for the Prediction of Adverse Drug Reactions. Journal of Chemical Information and Modeling 2019;59(10):4120 View
  6. Spiro A, Fernández García J, Yanover C. Inferring new relations between medical entities using literature curated term co-occurrences. JAMIA Open 2019;2(3):378 View
  7. Adly A, Adly A, Adly M. Approaches Based on Artificial Intelligence and the Internet of Intelligent Things to Prevent the Spread of COVID-19: Scoping Review. Journal of Medical Internet Research 2020;22(8):e19104 View
  8. Nguyen D, Nguyen C, Mamitsuka H. A survey on adverse drug reaction studies: data, tasks and machine learning methods. Briefings in Bioinformatics 2021;22(1):164 View
  9. Bate A, Hobbiger S. Artificial Intelligence, Real-World Automation and the Safety of Medicines. Drug Safety 2021;44(2):125 View
  10. Piroozmand F, Mohammadipanah F, Sajedi H. Spectrum of deep learning algorithms in drug discovery. Chemical Biology & Drug Design 2020;96(3):886 View
  11. Yu Z, Wu Z, Li W, Liu G, Tang Y, Jonathan W. MetaADEDB 2.0: a comprehensive database on adverse drug events. Bioinformatics 2021;37(15):2221 View
  12. Shin H, Cha J, Lee C, Song H, Jeong H, Kim J, Lee S. The 2011–2020 Trends of Data-Driven Approaches in Medical Informatics for Active Pharmacovigilance. Applied Sciences 2021;11(5):2249 View
  13. Bose K, Dutta S, Bose K. Remodelling structure-based drug design using machine learning. Emerging Topics in Life Sciences 2021;5(1):13 View
  14. Lee C, Chen Y. Descriptive prediction of drug side‐effects using a hybrid deep learning model. International Journal of Intelligent Systems 2021;36(6):2491 View
  15. Hwang M, Shin J, Seo H, Im J, Cho H, Bilal M. KoRASA: Pipeline Optimization for Open-Source Korean Natural Language Understanding Framework Based on Deep Learning. Mobile Information Systems 2021;2021:1 View
  16. Joshi P, Vedhanayagam M, Ramesh R. An Ensembled SVM Based Approach for Predicting Adverse Drug Reactions. Current Bioinformatics 2021;16(3):422 View
  17. Verman S, Anjankar A. A Narrative Review of Adverse Event Detection, Monitoring, and Prevention in Indian Hospitals. Cureus 2022 View
  18. Hariry R, Barenji R, Paradkar A. Towards Pharma 4.0 in clinical trials: A future-orientated perspective. Drug Discovery Today 2022;27(1):315 View
  19. Ishikawa T, Yakoh T, Urushihara H. An NLP-Inspired Data Augmentation Method for Adverse Event Prediction Using an Imbalanced Healthcare Dataset. IEEE Access 2022;10:81166 View
  20. Alpay B, Gosink M, Aguiar D. Evaluating molecular fingerprint-based models of drug side effects against a statistical control. Drug Discovery Today 2022;27(11):103364 View
  21. Das P, Yogita , Pal V. Integrative analysis of chemical properties and functions of drugs for adverse drug reaction prediction based on multi-label deep neural network. Journal of Integrative Bioinformatics 2022;19(3) View
  22. Askr H, Elgeldawi E, Aboul Ella H, Elshaier Y, Gomaa M, Hassanien A. Deep learning in drug discovery: an integrative review and future challenges. Artificial Intelligence Review 2022 View
  23. Joshi P, V M, Mukherjee A. A knowledge graph embedding based approach to predict the adverse drug reactions using a deep neural network. Journal of Biomedical Informatics 2022;132:104122 View
  24. Sato H. Development of Clinical Pharmaceutical Services <i>via</i> Artificial Intelligence Adaptation. YAKUGAKU ZASSHI 2022;142(4):337 View
  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 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
  31. Malec S, Wei P, Bernstam E, Boyce R, Cohen T. Using computable knowledge mined from the literature to elucidate confounders for EHR-based pharmacovigilance. Journal of Biomedical Informatics 2021;117:103719 View
  32. Yu Z, Wu Z, Li W, Liu G, Tang Y. ADENet: a novel network-based inference method for prediction of drug adverse events. Briefings in Bioinformatics 2022;23(2) View
  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
  34. Dabare R, Wong K, Shiratuddin M, Koutsakis P. A fuzzy data augmentation technique to improve regularisation. International Journal of Intelligent Systems 2022;37(8):4561 View
  35. Soh J, Park S, Lee H. HIDTI: integration of heterogeneous information to predict drug-target interactions. Scientific Reports 2022;12(1) View
  36. Ali Z, Alturise F, Alkhalifah T, Khan Y, Zhou X. IGPred-HDnet: Prediction of Immunoglobulin Proteins Using Graphical Features and the Hierarchal Deep Learning-Based Approach. Computational Intelligence and Neuroscience 2023;2023:1 View
  37. Knisely B, Hatim Q, Vaughn-Cooke M. Utilizing Deep Learning for Detecting Adverse Drug Events in Structured and Unstructured Regulatory Drug Data Sets. Pharmaceutical Medicine 2022;36(5):307 View
  38. Das P, Mazumder D. MLCNN‐COV: A multilabel convolutional neural network‐based framework to identify negative COVID medicine responses from the chemical three‐dimensional conformer. ETRI Journal 2023 View

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