Home Research Papers Datasets State of the Art Pricing

Discover, visualize, and connect AI research papers. Explore the latest trends and insights in artificial intelligence research.

Product

Home
Research Papers
About

Support

Contact
Terms of Service
Privacy Policy

medical

Clinical Concept Extraction

3260 papers • 126 benchmarks • 313 datasets

Automatic extraction of clinical named entities such as clinical problems, treatments, tests and anatomical parts from clinical notes. ( Source )

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in clinical-concept-extraction-1

Trend

Dataset

Best Model

Actions

2010 i2b2/VA

Libraries

Use these libraries to find clinical-concept-extraction-1 models and implementations

Datasets

2010 i2b2/VA

Adverse Drug Events (ADE) Corpus

2018 n2c2 (Track 2) - Adverse Drug Events and Medication Extraction

Toronto NeuroFace Dataset

Subtasks

Clinical Information Retreival

Most implemented papers

CharacterBERT: Reconciling ELMo and BERT for Word-Level Open-Vocabulary Representations From Characters

Junichi Tsujii, Pierre Zweigenbaum, T. Lavergne, Hiroshi Noji, Olivier Ferret, Hicham El Boukkouri•Mon Oct 19 2020

This work proposes CharacterBERT, a new variant of BERT that drops the wordpiece system altogether and uses a Character-CNN module instead to represent entire words by consulting their characters, and shows that this new model improves the performance of Bert on a variety of medical domain tasks while at the same time producing robust, word-level, and open-vocabulary representations.

Content

Introduction Benchmarks Datasets Subtasks Libraries Papers

171 0

Paper Graph

Bidirectional LSTM-CRF for Clinical Concept Extraction

Raghavendra Chalapathy, E. Z. Borzeshi, M. Piccardi•Tue Oct 18 2016

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 that outperform all recent methods and ranks closely to the best submission from the original 2010 i2b2/VA challenge.

96 0

Paper Graph

Recurrent neural networks with specialized word embeddings for health-domain named-entity recognition

Inigo Jauregi Unanue, E. Z. Borzeshi, M. Piccardi•Wed Jun 28 2017

This work presents a state-of-the-art system for DNR and CCE that avoids conventional, time-consuming feature engineering, and outperformed all previously proposed systems with the Bidirectional LSTM-CRF model.

121 0

Paper Graph

Clinical Concept Extraction with Contextual Word Embedding

Henghui Zhu, I. Paschalidis, A. Tahmasebi•Tue Oct 23 2018

The 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.

78 0

Paper Graph

Embedding Strategies for Specialized Domains: Application to Clinical Entity Recognition

Pierre Zweigenbaum, T. Lavergne, Olivier Ferret, Hicham El Boukkouri•Sat Jul 27 2019

This work shows that by combining off-the-shelf contextual embeddings (ELMo) with static word2vecembeddings trained on a small in-domain corpus built from the task data, they manage to reach and sometimes outperform representations learned from a large corpus in the medical domain.

18 0

Paper Graph

Improving Clinical Document Understanding on COVID-19 Research with Spark NLP

Veysel Kocaman, D. Talby•Sun Dec 06 2020

A clinical text mining system that improves on previous efforts in three ways, and can recognize over 100 different entity types including social determinants of health, anatomy, risk factors, and adverse events in addition to other commonly used clinical and biomedical entities.

12 0

Paper Graph

CLIN-X: pre-trained language models and a study on cross-task transfer for concept extraction in the clinical domain

D. Klakow, Jannik Strotgen, Heike Adel, Lukas Lange•Wed Dec 15 2021

The results highlight the importance of specialized language models, such as CLIN-X, for concept extraction in non-standard domains, but also show that the task-agnostic model architecture is robust across the tested tasks and languages so that domain- or task-specific adaptations are not required.

24 0

Paper Graph

Adding a benchmark result helps the community track progress.

Clinical Concept Extraction | State-of-the-Art