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

Machine Learning (1338) Clinical Informatics (852) New Methods (875) Pharmacovigilance (117) Natural Language Processing (611) Prevention and Health Promotion (823)

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

Authors of this article:

Soham Dasgupta1 Author Orcid Image ;   Aishwarya Jayagopal2 Author Orcid Image ;   Abel Lim Jun Hong2 Author Orcid Image ;   Ragunathan Mariappan2 Author Orcid Image ;   Vaibhav Rajan3 Author Orcid Image
Article Authors Cited by (4) Tweetations (6) Metrics

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