Bidirectional LSTM-CRF for Clinical Concept Extraction

Understand

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.