  Automated extraction of concepts from patient clinical records is an
essential facilitator of clinical research. For this reason, the 2010 i2b2/VA
Natural Language Processing Challenges for Clinical Records introduced a
concept extraction task aimed at identifying and classifying concepts into
predefined categories (i.e., treatments, tests and problems). State-of-the-art
concept extraction approaches heavily rely on handcrafted features and
domain-specific resources which are hard to collect and define. For this
reason, this paper proposes an alternative, streamlined approach: a recurrent
neural network (the bidirectional LSTM with CRF decoding) initialized with
general-purpose, off-the-shelf word embeddings. The experimental results
achieved on the 2010 i2b2/VA reference corpora using the proposed framework
outperform all recent methods and ranks closely to the best submission from the
original 2010 i2b2/VA challenge.
