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