# CliNER 2.0: Accessible and Accurate Clinical Concept Extraction

arXiv: https://arxiv.org/abs/1803.02245 · HTML: https://mlsanity.com/paper/1803.02245/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Mar 2018.

## Built on by (most cited first, 4 of 4)

- [NEAR: Named Entity and Attribute Recognition of clinical concepts](https://mlsanity.com/paper/2208.13949/) (2022)
- [DataWords: Getting Contrarian with Text, Structured Data and Explanations](https://mlsanity.com/paper/2111.05384/) (2021)
- [Epistemic AI platform accelerates innovation by connecting biomedical knowledge](https://mlsanity.com/paper/2201.11331/) (2022)
- [Aligning Proteins and Language: A Foundation Model for Protein Retrieval](https://mlsanity.com/paper/2506.08023/) (2025)

## Abstract (arXiv)

Clinical notes often describe important aspects of a patient's stay and are therefore critical to medical research. Clinical concept extraction (CCE) of named entities - such as problems, tests, and treatments - aids in forming an understanding of notes and provides a foundation for many downstream clinical decision-making tasks. Historically, this task has been posed as a standard named entity recognition (NER) sequence tagging problem, and solved with feature-based methods using handengineered domain knowledge. Recent advances, however, have demonstrated the efficacy of LSTM-based models for NER tasks, including CCE. This work presents CliNER 2.0, a simple-to-install, open-source tool for extracting concepts from clinical text. CliNER 2.0 uses a word- and character- level LSTM model, and achieves state-of-the-art performance. For ease of use, the tool also includes pre-trained models available for public use.