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 language||English (US)|
|Number of pages||7|
|Journal||International Journal of Medical Engineering and Informatics|
|State||Published - Mar 2011|
All Science Journal Classification (ASJC) codes
- Medicine (miscellaneous)
- Biomedical Engineering
- Health Informatics
- Adverse drug reactions
- Artificial neural network
- Feed-forward back-propagation.
- Levenberg-Marquardt algorithm
- Logistics regression
- Multiple layers
- Supervised training