# Clinical Concept Extraction with Contextual Word Embedding

Source: [Clinical Concept Extraction with Contextual Word Embedding](<https://www.sota2.com/research/paper/68fa651d1e209131cea08714>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:19:52.933Z

Authors: Henghui Zhu, Ioannis Ch. Paschalidis, Amir Tahmasebi

Published: 2018-10-24T18:18:19.000Z

## Abstract

Automatic extraction of clinical concepts is an essential step for turning the unstructured data within a clinical note into structured and actionable information. In this work, we propose a clinical concept extraction model for automatic annotation of clinical problems, treatments, and tests in clinical notes utilizing domain-specific contextual word embedding. A contextual word embedding model is first trained on a corpus with a mixture of clinical reports and relevant Wikipedia pages in the clinical domain. Next, a bidirectional LSTM-CRF model is trained for clinical concept extraction using the contextual word embedding model. We tested our proposed model on the I2B2 2010 challenge dataset. Our proposed model achieved the best performance among reported baseline models and outperformed the state-of-the-art models by 3.4% in terms of F1-score.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Clinical Concept Extraction on i2b2 2010 (test)](<https://www.sota2.com/research/sota/clinical-concept-extraction-on-i2b2-2010-test.md>): Clinical concept extraction; i2b2 2010 (test); Exact F1: 88.6.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1810.10566>)

[PDF](<https://arxiv.org/pdf/1810.10566.pdf>)