Abstract
Information on changes in a drug's effect when taken in combination with a second drug, known as drug-drug interaction (DDI), is relevant in the pharmaceutical industry. DDIs can delay, decrease, or enhance absorption of either drug and thus decrease or increase their action or cause adverse effects. Information Extraction (IE) can be of great benefit in allowing identification and extraction of relevant information on DDIs. We here propose an approach for the extraction of DDI from text using neural word embedding to train a machine learning system. Results show that our system is competitive against other systems for the task of extracting DDIs, and that significant improvements can be achieved by learning from word features and using a deep-learning approach. Our study demonstrates that machine learning techniques such as neural networks and deep learning methods can efficiently aid in IE from text. Our proposed approach is well suited to play a significant role in future research.
Highlights
Recent research demonstrates an increasing interest in applying machine learning and natural language processing to drug–drug interactions (DDIs)
The three metrics are calculated for each DDI types as follows: Precision is the proportion of DDIs found by the learning system that are correct; i.e. the ratio between the number of DDIs correctly detected and the total number of DDIs found by the system
Recall is the proportion of DDIs presented in the corpus that are found by the system; i.e. the ratio between the number of DDIs correctly detected and the total number of drug entities in the gold standard
Summary
Recent research demonstrates an increasing interest in applying machine learning and natural language processing to drug–drug interactions (DDIs). An added drug may increase or decrease the e®ect of the initial drug, or it may lead to an adverse e®ect that is not normally associated with either drug. As such adverse drug reactions (ADRs) may lead to an. Ceesay increase in drug-safety incidents and healthcare costs, their prevention is of interest.[12] There is great potential benet in extracting DDI information from biomedical texts, using information extraction (IE) techniques. Databases currently listing known DDIs include Dailymed,[11] DrugBank[34] and Medscape.[21]
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