Bidirectional LSTM-CRF for Clinical Concept Extraction
This work addresses the need for efficient clinical concept extraction to facilitate research, offering a streamlined alternative to feature-heavy methods, though it is incremental as it builds on existing neural network techniques.
The paper tackled the problem of automated extraction of clinical concepts from patient records by proposing a bidirectional LSTM-CRF model with general-purpose word embeddings, which outperformed recent methods and ranked closely to the best submission from the 2010 i2b2/VA challenge.
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.