A Rich Feature-based Kernel Approach for Drug- Drug Interaction Extraction

Discovering drug-drug interactions (DDIs) is a crucial issue for both patient safety and health care cost control. Developing text mining techniques for identifying DDIs has attracted a great deal of attention in the last few years. Unfortunately, state-of-the-art results didn't exceed the threshold of 0.7 F1 score, which calls for more efforts. In this work, we propose a new feature-based kernel method to extract and classify DDIs. Our approach consists of two steps: identifying DDIs and assigning one of four different DDI types to the predicted drug pairs. We demonstrate that by using new groups of features non-linear kernels can achieve the best performance. When evaluated on the DDIExtraction 2013 challenge corpus, our system achieved an F1-score of 71.79%, as compared to 69.75% and 68.4% reported by the top two state-of-the-art systems.

@article{RAIHANI2017,
  title     = {A Rich Feature-based Kernel Approach for Drug- Drug Interaction Extraction},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {8},
  number    = {4},
  year      = {2017},
  publisher = {The Science and Information Organization},
  author    = {ANASS RAIHANI and NABIL LAACHFOUBI},
  doi       = {10.14569/IJACSA.2017.080445},
  url       = {https://doi.org/10.14569/IJACSA.2017.080445}
}