# Bidirectional LSTM-CRF for Clinical Concept Extraction

arXiv: https://arxiv.org/abs/1611.08373 · HTML: https://mlsanity.com/paper/1611.08373/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2016.

## Built on by (most cited first, 9 of 9)

- [Neural Natural Language Processing for Unstructured Data in Electronic Health Records: a Review](https://mlsanity.com/paper/2107.02975/) (2021)
- [Multimodal data matters: language model pre-training over structured and unstructured electronic health records](https://mlsanity.com/paper/2201.10113/) (2022)
- [NEAR: Named Entity and Attribute Recognition of clinical concepts](https://mlsanity.com/paper/2208.13949/) (2022)
- [BURExtract-Llama: An LLM for Clinical Concept Extraction in Breast Ultrasound Reports](https://mlsanity.com/paper/2408.11334/) (2024)
- [Neural Entity Recognition with Gazetteer based Fusion](https://mlsanity.com/paper/2105.13225/) (2021)
- [Identification of Pediatric Respiratory Diseases Using Fine-grained Diagnosis System](https://mlsanity.com/paper/2108.10818/) (2021)
- [TAG: Toward Accurate Social Media Content Tagging with a Concept Graph](https://mlsanity.com/paper/2110.06892/) (2021)
- [OEMA: Ontology-Enhanced Multi-Agent Collaboration Framework for Zero-Shot Clinical Named Entity Recognition](https://mlsanity.com/paper/2511.15211/) (2025)
- [Leveraging large language models for SQL behavior-based database intrusion detection](https://mlsanity.com/paper/2508.05690/) (2025)

## Abstract (arXiv)

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.