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
.
Journals
- 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
- 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
- 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
- Rohani N, Eslahchi C. Drug-Drug Interaction Predicting by Neural Network Using Integrated Similarity. Scientific Reports 2019;9(1) View
- 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
- 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
- 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
- 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
- Bate A, Hobbiger S. Artificial Intelligence, Real-World Automation and the Safety of Medicines. Drug Safety 2021;44(2):125 View
- Piroozmand F, Mohammadipanah F, Sajedi H. Spectrum of deep learning algorithms in drug discovery. Chemical Biology & Drug Design 2020;96(3):886 View
- 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
- 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
- Bose K, Dutta S, Bose K. Remodelling structure-based drug design using machine learning. Emerging Topics in Life Sciences 2021;5(1):13 View
- 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
- 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
- Joshi P, Vedhanayagam M, Ramesh R. An Ensembled SVM Based Approach for Predicting Adverse Drug Reactions. Current Bioinformatics 2021;16(3):422 View
- Verman S, Anjankar A. A Narrative Review of Adverse Event Detection, Monitoring, and Prevention in Indian Hospitals. Cureus 2022 View
- 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
- 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
- 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
- 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
- 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 2023;56(7):5975 View
- 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
- Sato H. Development of Clinical Pharmaceutical Services <i>via</i> Artificial Intelligence Adaptation. YAKUGAKU ZASSHI 2022;142(4):337 View
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Soh J, Park S, Lee H. HIDTI: integration of heterogeneous information to predict drug-target interactions. Scientific Reports 2022;12(1) View
- 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
- 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
- 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 2024;46(2):290 View
- Zhao H, Ni P, Zhao Q, Liang X, Ai D, Erhardt S, Wang J, Li Y, Wang J. Identifying the serious clinical outcomes of adverse reactions to drugs by a multi-task deep learning framework. Communications Biology 2023;6(1) View
- Das P, Thakran Y, Anal S, Pal V, Yadav A. BRMCF: Binary Relevance and MLSMOTE Based Computational Framework to Predict Drug Functions From Chemical and Biological Properties of Drugs. IEEE/ACM Transactions on Computational Biology and Bioinformatics 2023;20(3):1761 View
- Zhang L, Liu Y, Tian J. Patient Preferences and Their Influence on Chronic Hepatitis B-A Review. Patient Preference and Adherence 2023;Volume 17:3119 View
- Arshed M, Ibrahim M, Mumtaz S, Tanveer M, Ahmed S. Chem2Side: A Deep Learning Model with Ensemble Augmentation (Conventional + Pix2Pix) for COVID-19 Drug Side-Effects Prediction from Chemical Images. Information 2023;14(12):663 View
- Dauner D, Leal E, Adam T, Zhang R, Farley J. Evaluation of four machine learning models for signal detection. Therapeutic Advances in Drug Safety 2023;14 View
- Zhao W, Yao W, Jiang X, He T, Shi C, Hu X. An Explainable Framework for Predicting Drug-Side Effect Associations via Meta-Path-Based Feature Learning in Heterogeneous Information Network. IEEE/ACM Transactions on Computational Biology and Bioinformatics 2023;20(6):3635 View
- Uner O, Kuru H, Cinbis R, Tastan O, Cicek A. DeepSide: A Deep Learning Approach for Drug Side Effect Prediction. IEEE/ACM Transactions on Computational Biology and Bioinformatics 2023;20(1):330 View
- 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
- Nafea A, Ibrahim M, Mukhlif A, AL-Ani M, Omar N. An Ensemble Model for Detection of Adverse Drug Reactions. ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY 2024;12(1):41 View
- Das P, Mazumder D. Inceptionv3‐LSTM‐COV: A multi‐label framework for identifying adverse reactions to COVID medicine from chemical conformers based on Inceptionv3 and long short‐term memory. ETRI Journal 2024 View
- Das P, Mazumder D. Advances in Predicting Drug Functions: A Decade-Long Survey in Drug Discovery Research. IEEE Transactions on Molecular, Biological, and Multi-Scale Communications 2024;10(1):75 View
- Nafea A, AL-Mahdawi M, AL-Ani M, Omar N. A Review on Adverse Drug Reaction Detection Techniques. ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY 2024;12(1):143 View
- Funari A, Fiscon G, Paci P. Network medicine and systems pharmacology approaches to predicting adverse drug effects. British Journal of Pharmacology 2024 View
- Farnoush A, Sedighi-Maman Z, Rasoolian B, Heath J, Fallah B. Prediction of adverse drug reactions using demographic and non-clinical drug characteristics in FAERS data. Scientific Reports 2024;14(1) View
- Yao W, Wei A, Xiao Z, Zhao W, Shen X, Jiang X, He T. An Improved Framework for Drug-Side Effect Associations Prediction via Counterfactual Inference-Based Data Augmentation. IEEE Transactions on NanoBioscience 2024;23(4):540 View
Books/Policy Documents
- Dabare R, Wong K, Shiratuddin M, Koutsakis P. Neural Information Processing. View
- Nova S, Rahman M, Hosen A. Rhythms in Healthcare. View
- Das P, Sangma J, Pal V, Yogita . Practical Applications of Computational Biology & Bioinformatics, 15th International Conference (PACBB 2021). View
- Dabare R, Wong K, Shiratuddin M, Koutsakis P. Neural Information Processing. View
- Piroozmand F, Mohammadipanah F, Sajedi H. A Handbook of Artificial Intelligence in Drug Delivery. View
- Montero-Colio M, Salas-Zárate M, Paredes-Valverde M. Technologies and Innovation. View
- Dey A, Shrivastava J, Kumar C. Intelligent Human Centered Computing. View
- Das P, Mazumder D. Mathematical Modeling and Intelligent Control for Combating Pandemics. View
- Gupta S, Laghuvarapu S, Priyakumar U. Artificial Intelligence in Healthcare. View