Abstract: 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.

ANASS RAIHANI and NABIL LAACHFOUBI, “A Rich Feature-based Kernel Approach for Drug- Drug Interaction Extraction” International Journal of Advanced Computer Science and Applications(IJACSA), 8(4), 2017. http://dx.doi.org/10.14569/IJACSA.2017.080445

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