Abstract
Drug-drug interaction (DDI) extraction as a typical relation extraction task in natural language processing (NLP) has always attracted great attention. Most state-of-the-art DDI extraction systems are based on support vector machines (SVM) with a large number of manually defined features. Recently, convolutional neural networks (CNN), a robust machine learning method which almost does not need manually defined features, has exhibited great potential for many NLP tasks. It is worth employing CNN for DDI extraction, which has never been investigated. We proposed a CNN-based method for DDI extraction. Experiments conducted on the 2013 DDIExtraction challenge corpus demonstrate that CNN is a good choice for DDI extraction. The CNN-based DDI extraction method achieves an F-score of 69.75%, which outperforms the existing best performing method by 2.75%.
Highlights
Drug-drug interactions (DDIs) occur when two or more drugs are taken in combination that alters the way one or more drugs act in human body and may result in unexpected side effects
The difference between the F-scores on these two types of DDIs achieves 31.37% (77.75% versus 46.38%). Both position embeddings and negative instance filtering improve the overall performance of the convolutional neural networks (CNN)-based DDI extraction system
We propose a CNN-based method for DDI extraction
Summary
Drug-drug interactions (DDIs) occur when two or more drugs are taken in combination that alters the way one or more drugs act in human body and may result in unexpected side effects. The unexpected side effects caused by DDIs are always very dangerous (may lead to deaths) and greatly increase healthcare costs. The more DDIs healthcare professionals know, the less medical accidents occur. DDIs have always been attracting much attention in drug safety and healthcare management [1]. There are several publicly available databases supporting healthcare professionals to find DDIs. For example, DrugBank [2], which is an online drug database, consists of 8311 drugs entries. Each drug entry contains more than 200 fields, including a DDI field. The DDIs in these databases cannot be directly accessed like relational databases by healthcare professionals. New DDIs are often detected by healthcare professionals and presented in literature, including scientific articles, books, and technical reports [3]. DDI extraction, which detects DDIs in unstructured text and classifies them into predefined categories automatically, has become an increasing interest in medical text mining
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