Published on in Vol 9, No 10 (2021): October

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/32730, first published .
Adverse Drug Event Prediction Using Noisy Literature-Derived Knowledge Graphs: Algorithm Development and Validation

Adverse Drug Event Prediction Using Noisy Literature-Derived Knowledge Graphs: Algorithm Development and Validation

Adverse Drug Event Prediction Using Noisy Literature-Derived Knowledge Graphs: Algorithm Development and Validation

Journals

  1. Mariappan R, Jayagopal A, Sien H, Rajan V, Wren J. Neural Collective Matrix Factorization for integrated analysis of heterogeneous biomedical data. Bioinformatics 2022;38(19):4554 View
  2. Kumar S, Nanelia A, Mariappan R, Rajagopal A, Rajan V. Patient Representation Learning From Heterogeneous Data Sources and Knowledge Graphs Using Deep Collective Matrix Factorization: Evaluation Study. JMIR Medical Informatics 2022;10(1):e28842 View
  3. Liang L, Hu J, Sun G, Hong N, Wu G, He Y, Li Y, Hao T, Liu L, Gong M. Artificial Intelligence-Based Pharmacovigilance in the Setting of Limited Resources. Drug Safety 2022;45(5):511 View
  4. Patera A, Maidment J, Maroj B, Mohamed A, Twomey K. A Science-Based Methodology Framework for the Assessment of Combination Safety Risks in Clinical Trials. Pharmaceutical Medicine 2023;37(3):183 View
  5. Ghanvatkar S, Rajan V. Evaluating Explanations From AI Algorithms for Clinical Decision-Making: A Social Science-Based Approach. IEEE Journal of Biomedical and Health Informatics 2024;28(7):4269 View
  6. Hauben M, Rafi M, Abdelaziz I, Hassanzadeh O. Knowledge Graphs in Pharmacovigilance: A Scoping Review. Clinical Therapeutics 2024;46(7):544 View