Bidirectional LSTM-CRF for Clinical Concept Extraction
arXiv:1610.05858
Abstract
Extraction of concepts present in patient clinical records is an essential step in clinical research. The 2010 i2b2/VA Workshop on Natural Language Processing Challenges for clinical records presented concept extraction (CE) task, with aim to identify concepts (such as treatments, tests, problems) and classify them into predefined categories. State-of-the-art CE approaches heavily rely on hand crafted features and domain specific resources which are hard to collect and tune. For this reason, this paper employs bidirectional LSTM with CRF decoding initialized with general purpose off-the-shelf word embeddings for CE. The experimental results achieved on 2010 i2b2/VA reference standard corpora using bidirectional LSTM CRF ranks closely with top ranked systems.
This paper "Bidirectional LSTM-CRF for Clinical Concept Extraction" is accepted for short paper presentation at Clinical Natural Language Processing Workshop at COLING 2016 Osaka, Japan. December 11, 2016
References in corpus (1)
Cited by in corpus (11)
- Deep Learning for Anomaly Detection: A Survey
- Enhancing Clinical Concept Extraction with Contextual Embeddings
- Recurrent neural networks with specialized word embeddings for health-domain named-entity recognition
- SECNLP: A Survey of Embeddings in Clinical Natural Language Processing
- Joint Entity Extraction and Assertion Detection for Clinical Text
- Clinical Concept Extraction with Contextual Word Embedding
- Deep Learning Approaches for Extracting Adverse Events and Indications of Dietary Supplements from Clinical Text
- Identification of Pediatric Respiratory Diseases Using Fine-grained Diagnosis System
- CliNER 2.0: Accessible and Accurate Clinical Concept Extraction
- Deep Learning for Insider Threat Detection: Review, Challenges and Opportunities
- Neural Entity Recognition with Gazetteer based Fusion