Title: A hybrid deep learning approach for medical relation extraction,
Abstract: Mining relationships between treatment(s) and medical problem(s) is vital in
the biomedical domain. This helps in various applications, such as decision
support system, safety surveillance, and new treatment discovery. We propose a
deep learning approach that utilizes both word level and sentence-level
representations to extract the relationships between treatment and problem.
While deep learning techniques demand a large amount of data for training, we
make use of a rule-based system particularly for relationship classes with
fewer samples. Our final relations are derived by jointly combining the results
from deep learning and rule-based models. Our system achieved a promising
performance on the relationship classes of I2b2 2010 relation extraction task.