Prediction of the serious adverse drug reactions using an artificial neural network model

Peng Fang Yen, Dinesh P. Mital, Shankar Srinivasan

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


The objective of the work reported in this paper was to develop a model that predicts the serious adverse drug reactions (ADRs) on medication uses. The predictive model is developed using the feed-forward back-propagation type of artificial neural network (ANN) using the Levenberg-Marquardt algorithm. The target and input data of the ANN model are derived from ADR data in FDA's adverse event reporting system. The target data contain the serious and non-serious ADRs. An ADR dataset consisting of 3,164 observations is used to obtain preliminary results. The preliminary results show that the ANN model provides 99.87% accuracy with the sensitivity of 99.11% for the serious ADRs and the specificity of 100% for the non-serious ADRs. These preliminary results will be further verified by a research using an ADR dataset containing 10,000 observations.

Original languageEnglish (US)
Pages (from-to)53-59
Number of pages7
JournalInternational Journal of Medical Engineering and Informatics
Issue number1
StatePublished - Mar 2011

All Science Journal Classification (ASJC) codes

  • Medicine (miscellaneous)
  • Biomaterials
  • Biomedical Engineering
  • Health Informatics


  • ADRs
  • ANN
  • Accuracy
  • Adverse drug reactions
  • Artificial neural network
  • Feed-forward back-propagation.
  • Levenberg-Marquardt algorithm
  • Logistics regression
  • Multiple layers
  • Prediction
  • Sensitivity
  • Specificity
  • Supervised training


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