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

Authors: Peng-fang Yen, Dinesh P. Mital, Shankar Srinivasan

Addresses: Department of Health Informatics, School of Health Related Professions, University of Medicine and Dentistry of New Jersey, Room 350, 65 Bergen Street, Newark, NJ 07077, USA. ' Department of Health Informatics, School of Health Related Professions, University of Medicine and Dentistry of New Jersey, Room 350, 65 Bergen Street, Newark, NJ 07077, USA. ' Department of Health Informatics, School of Health Related Professions, University of Medicine and Dentistry of New Jersey, Room 350, 65 Bergen Street, Newark, NJ 07077, USA

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.

Keywords: adverse drug reactions; ADRs; artificial neural networks; ANNs; supervised training; Levenberg-Marquardt algorithm; logistics regression; multiple layers; prediction; accuracy; sensitivity; specificity; feedforward backpropagation; medication; adverse reactions.

DOI: 10.1504/IJMEI.2011.039076

International Journal of Medical Engineering and Informatics, 2011 Vol.3 No.1, pp.53 - 59

Published online: 28 Feb 2015 *

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