Prediction of the serious adverse drug reactions using an artificial neural network model
by Peng-fang Yen, Dinesh P. Mital, Shankar Srinivasan
International Journal of Medical Engineering and Informatics (IJMEI), Vol. 3, No. 1, 2011

Abstract: 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.

Online publication date: Sat, 28-Feb-2015

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Medical Engineering and Informatics (IJMEI):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com