Abstract: Drug-drug interaction (DDI) is a vital information when physicians and pharmacists intend to co-administer two or more drugs. Thus, several DDI databases are constructed to avoid mistakenly combined use. In recent years, automatically extracting DDIs from biomedical text has drawn researchers' attention. However, the existing work utilize either complex feature engineering or NLP tools, both of which are insufficient for sentence comprehension. Inspired by the deep learning approaches in natural language processing, we propose a recur- rent neural network model with multiple attention layers for DDI classification. We evaluate our model on 2013 SemEval DDIExtraction dataset. The experiments show that our model classifies most of the drug pairs into correct DDI categories, which outperforms the existing NLP or deep learning methods.

Introduction: Drug-drug interaction (DDI) is a situation when one drug increases or decreases the effect of another drug BIBREF0 . Adverse drug reactions may cause severe side effect, if two or more medicines were taken and their DDI were not investigated in detail. DDI is a common cause of illness, even a cause of death BIBREF1 . Thus, DDI databases for clinical medication decisions are proposed by some researchers. These databases such as SFINX BIBREF2 , KEGG BIBREF3 , CredibleMeds BIBREF4 help physicians and pharmacists avoid most adverse drug reactions. Traditional DDI databases are manually constructed according to clinical records, scientific research and drug specifications. For instance, The sentence “With combined use, clinicians should be aware, when phenytoin is added, of the potential for reexacerbation of pulmonary symptomatology due to lowered serum theophylline concentrations BIBREF5 ”, which is from a pharmacotherapy report, describe the side effect of phenytoin and theophylline's combined use. Then this information on specific medicines will be added to DDI databases. As drug-drug interactions have being increasingly found, manually constructing DDI database would consume a lot of manpower and resources. There has been many efforts to automatically extract DDIs from natural language BIBREF0 , BIBREF6 , BIBREF7 , BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 , BIBREF12 , mainly medical literature and clinical records. These works can be divided into the following categories: To avoid complex feature engineering and NLP toolkits' usage, we employ deep learning approaches for sentence comprehension as a whole. Our model takes in a sentence from biomedical literature which contains a drug pair and outputs what kind of DDI this drug pair belongs. This assists physicians refrain from improper combined use of drugs. In addition, the word and sentence level attentions are introduced to our model for better DDI predictions. We train our language comprehension model with labeled instances. Figure FIGREF5 shows partial records in DDI corpus BIBREF16 . We extract the sentence and drug pairs in the records. There are 3 drug pairs in this example thus we have 3 instances. The DDI corpus annotate each drug pair in the sentence with a DDI type. The DDI type, which is the most concerned information, is described in table TABREF4 . The details about how we train our model and extract the DDI type from text are described in the remaining sections.

Question: What is the performance of their model?
Answer: Answer with content missing: (Table II) Proposed model has F1 score of  0.7220.