SOTAVerified

Clinical Concept Extraction

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

( Source )

Papers

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Showing 1–24 of 24 papers

TitleStatusHype
Accurate clinical and biomedical Named entity recognition at scaleCode3
CharacterBERT: Reconciling ELMo and BERT for Word-Level Open-Vocabulary Representations From CharactersCode1
Clinical Concept Extraction: a Methodology Review—0
BURExtract-Llama: An LLM for Clinical Concept Extraction in Breast Ultrasound Reports—0
ThinkMiners: Disorder Recognition using Conditional Random Fields and Distributional Semantics—0
Analysis of Word Embeddings and Sequence Features for Clinical Information Extraction—0
Enhancing Clinical Concept Extraction with Contextual Embeddings—0
Extracting clinical concepts from user queries—0
GatorTron: A Large Clinical Language Model to Unlock Patient Information from Unstructured Electronic Health Records—0
Identifying Risk Factors For Heart Disease in Electronic Medical Records: A Deep Learning Approach—0
Machine-learned solutions for three stages of clinical information extraction: the state of the art at i2b2 2010—0
NLNDE at CANTEMIST: Neural Sequence Labeling and Parsing Approaches for Clinical Concept Extraction—0
Selective Attention Federated Learning: Improving Privacy and Efficiency for Clinical Text Classification—0
CliNER 2.0: Accessible and Accurate Clinical Concept Extraction—0
Clinical Concept and Relation Extraction Using Prompt-based Machine Reading Comprehension—0
Clinical Concept Extraction for Document-Level Coding—0
Cost-effective Selection of Pretraining Data: A Case Study of Pretraining BERT on Social Media—0
Bidirectional LSTM-CRF for Clinical Concept ExtractionCode0
Embedding Strategies for Specialized Domains: Application to Clinical Entity RecognitionCode0
Bidirectional LSTM-CRF for Clinical Concept ExtractionCode0
Improving Clinical Document Understanding on COVID-19 Research with Spark NLPCode0
Clinical Concept Extraction with Contextual Word EmbeddingCode0
CLIN-X: pre-trained language models and a study on cross-task transfer for concept extraction in the clinical domainCode0
Recurrent neural networks with specialized word embeddings for health-domain named-entity recognitionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1BERTlarge (MIMIC)Exact Span F190.25—Unverified
2CharacterBERT (base, medical)Exact Span F189.24—Unverified
3ClinicalBERTExact Span F187.4—Unverified
4ELMo (finetuned on i2b2) + word2vec (i2b2)Exact Span F186.23—Unverified
5deBruijn et al. (System 1.1)Exact Span F185.23—Unverified