Mining drug-drug interactions for healthcare professionals

Abstract

The fourth leading cause of death in the US are Adverse Drug Reactions (ADRs)red. One such cause of ADRs is brought about through Drug-drug Interactions (DDIs). The positive side of this is that such reactions can be prevented. DDIs are reported during the pharmacovigilance (PV) process. PV is the practice of monitoring and detecting ADRs once a drug is launched into the market. Information related to DDIs is dispersed across different biomedical articles. We propose medicX, a system that is able to detect DDIs in biomedical texts by leveraging on different machine learning techniques. The main components within our system are the Drug Named Entity Recognition (DNER) component and the DDI Identification component. Different approaches were investigated in line with existing research. The DNER component is evaluated using the CHEMDNER and the DDIExtraction 2013 challenge corpora. Conversely, the DDI Identification component is evaluated using the DDIExtraction 2013 challenge corpus. The DNER component is implemented using an approach based on LSTM-CRF. This method achieves a macro-averaged F1-score of 84.89% when it is trained and evaluated on the DDI-2013 corpus, which is 1.43% higher than the system that placed first in the DDIExtraction 2013 challenge. On the other hand, the DDI Identification component is implemented using a two-stage rich feature-based linear-kernel SVM. This classifier achieves an F1-score of 66.18%, as compared to the SVM state-of-the-art DDI system that reported an F1-score of 71.79%.