Abstract
Information extraction (IE) is the process of automatically identifying structured information from unstructured or partially structured text. IE processes can involve several activities, such as named entity recognition, event extraction, relationship discovery, and document classification, with the overall goal of translating text into a more structured form. Information on the changes in the effect of a drug, when taken in combination with a second drug, is known as drug–drug interaction (DDI). DDIs can delay, decrease, or enhance absorption of drugs and thus decrease or increase their efficacy or cause adverse effects. Recent research trends have shown several adaptation of recurrent neural networks (RNNs) from text. In this study, we highlight significant challenges of using RNNs in biomedical text processing and propose automatic extraction of DDIs aiming at overcoming some challenges. Our results show that the system is competitive against other systems for the task of extracting DDIs.
Highlights
A pharmacological effect that occurs when a given drug is altered by the action of another drug, leading to unwanted clinical effects, is referred to as the drug–drug interaction (DDI) [1]
This paper explores the challenges using recurrent neural networks (RNNs) in determining and classifying DDIs from biomedical literature and proposes methods to overcome the shortcomings with RNN models and their derivatives
We evaluate the results of the system considering the following evaluation criteria that are used in SemEval 2013: 1) Macro evaluation: a DDI is correctly detected only if the system is able to assign the correct prediction and the correct type to it
Summary
A pharmacological effect that occurs when a given drug is altered by the action of another drug, leading to unwanted clinical effects, is referred to as the drug–drug interaction (DDI) [1]. Identifying DDIs is a major challenge in drug development. When drugs are co-administered, one drug may increase or decrease the effect of the other or lead to an unexpected effect. The 2011 [3] and 2013 [4] DDI Extraction challenges have been held to promote the implementation and comparative assessment of natural language processing techniques in the field of pharmacovigilance. In the 2013 challenge, the DDIs needed to be classified into four predefined DDI types: advice, effect, mechanism, and int [5]. “Advice” is assigned when a recommendation or advice regarding the concomitant use of two drugs is described.
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