A Hybrid Approach Based on Pattern Recognition and BioNLP for Investigating Drug-Drug Interaction
In the field of drug research and development, the investigation of drug-drug interactions
(DDIs) is a vital research area. Many clinical tools are used in the industry providing the broad lists of
DDIs. But these tools can return unpredictable results, only limited to specific types of interaction.
Also these tools lack the synchronized database of drug-drug interaction. In our research work, we are
proposing a novel pattern recognition based technique that investigates the patterns of natural language
processing for extraction of drug-drug interaction. The proposed technique is novel in the sense that is uses a smaller
feature set, computationally less expensive, yet yielding better results as compared to previous techniques. The proposed
technique is based on Bioinformatics and NLP (Natural Language processing). For this research paper, we have collected
biomedical data such as drug names, drug identification numbers (ID’s) and different types of drug-drug interaction
sentences from DrugBank(a free online resource). Through parsing the sentences, we investigated the patterns for drugdrug
interaction extraction. For performance evaluation of our proposed work, we applied three different types of
classification models i.e. Naïve Bayes, J48 (decision tree) and Random Forest. The results achieved by our technique are:
F-score 82.4%, Precision 81.6% and Recall 83.2%. The best accuracy achieved is with Random Forest, which is 99.0%.
Comparison with previous research shows that our proposed technique provides better results.
Keywords: Bioinformatics, biomedical, classification, drug, drug bank, drug-drug interaction, pattern recognition, NLP.
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