Bidirectional LSTM-CRF for Clinical Concept Extraction
By Raghavendra Chalapathy, Ehsan Zare Borzeshi, Massimo Piccardi. 2016
97 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Molecular Biology
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Yifan Peng
- Brett K. Beaulieu-Jones
- Zhiyong Lu
- Anshul B Kundaje
- Avanti Shrikumar
- Casey S. Greene
- Amr M. Alexandari
- S. Joshua Swamidass
In Molecular Biology
- Casey S. Greene
- Yifan Peng
- Brett K. Beaulieu-Jones
- G. Way
- Amr M. Alexandari
- Zhiyong Lu
- Avanti Shrikumar
- S. Joshua Swamidass
Citations in the corpus
- Opportunities and obstacles for deep learning in biology and medicine (2017, background): “In recent work, the authors evaluated the extent to which deep learning methods could be applied on top of generic features for domain-specific concept extraction [74].”
- Enhancing Clinical Concept Extraction with Contextual Embedding (2019, background): “Up to now, the most prominent model for clinical concept extraction is a bidirectional Long Short-Term Memory with Conditional Random Field (Bi-LSTM CRF) architecture (Chalapathy et al., 2016; Habibi et al., 2017; Florez et al., 2018).”
- Diagnostic Inferencing via Improving Clinical Concept Extraction with Deep Reinforcement Learning: A Preliminary Study (2017, background): “Existing clinical concept extraction tools mainly depend on large corpora of labeled examples and knowledge bases (Jonnalagadda et al., 2012; Kim et al., 2015; Chalapathy et al., 2016).”