Clinical Concept Extraction
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Benchmarks
2010 i2b2/VA
Most implemented
CharacterBERT: Reconciling ELMo and BERT for Word-Level Open-Vocabulary Representations From Characters
Accurate clinical and biomedical Named entity recognition at scale
CLIN-X: pre-trained language models and a study on cross-task transfer for concept extraction in the clinical domain
Embedding Strategies for Specialized Domains: Application to Clinical Entity Recognition
Clinical Concept Extraction with Contextual Word Embedding
Papers
Selective Attention Federated Learning: Improving Privacy and Efficiency for Clinical Text Classification
Federated Learning (FL) faces major challenges regarding communication overhead and model privacy when training large language models (LLMs), especially in healthcare applications. To address these, we introduce Selectiv…
Clinical Concept ExtractionFederated Learningtext-classificationText ClassificationBURExtract-Llama: An LLM for Clinical Concept Extraction in Breast Ultrasound Reports
Breast ultrasound is essential for detecting and diagnosing abnormalities, with radiology reports summarizing key findings like lesion characteristics and malignancy assessments. Extracting this critical information is c…
Clinical Concept ExtractionClinical Concept and Relation Extraction Using Prompt-based Machine Reading Comprehension
Objective: To develop a natural language processing system that solves both clinical concept extraction and relation extraction in a unified prompt-based machine reading comprehension (MRC) architecture with good general…
Clinical Concept ExtractionMachine Reading ComprehensionReading ComprehensionRelation+2Accurate clinical and biomedical Named entity recognition at scale
We introduce an agile, production-grade clinical and biomedical Named entity recognition (NER) algorithm based on a modified BiLSTM-CNN-Char DL architecture built on top of Apache Spark. Our NER implementation establishe…
Clinical Concept ExtractionDe-identificationnamed-entity-recognitionNamed Entity Recognition+2GatorTron: A Large Clinical Language Model to Unlock Patient Information from Unstructured Electronic Health Records
There is an increasing interest in developing artificial intelligence (AI) systems to process and interpret electronic health records (EHRs). Natural language processing (NLP) powered by pretrained language models is the…
Clinical Concept ExtractionLanguage ModelingLanguage ModellingMedical Question Answering+6CLIN-X: pre-trained language models and a study on cross-task transfer for concept extraction in the clinical domain
The field of natural language processing (NLP) has recently seen a large change towards using pre-trained language models for solving almost any task. Despite showing great improvements in benchmark datasets for various …
Clinical Concept ExtractionLanguage ModellingSentenceXLM-R