Papers Clinical Concept Extraction
“Clinical Concept Extraction” 태그가 달린 논문 24편 · 필터 해제
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-RImproving Clinical Document Understanding on COVID-19 Research with Spark NLP
Following the global COVID-19 pandemic, the number of scientific papers studying the virus has grown massively, leading to increased interest in automated literate review. We present a clinical text mining system that im…
AnatomyClinical Assertion Status DetectionClinical Concept Extractiondocument understanding+3NLNDE at CANTEMIST: Neural Sequence Labeling and Parsing Approaches for Clinical Concept Extraction
The recognition and normalization of clinical information, such as tumor morphology mentions, is an important, but complex process consisting of multiple subtasks. In this paper, we describe our system for the CANTEMIST …
Clinical Concept ExtractionCharacterBERT: Reconciling ELMo and BERT for Word-Level Open-Vocabulary Representations From Characters
Due to the compelling improvements brought by BERT, many recent representation models adopted the Transformer architecture as their main building block, consequently inheriting the wordpiece tokenization system despite i…
Clinical Concept ExtractionDrug–drug Interaction ExtractionNatural Language InferenceRelation Extraction+1Cost-effective Selection of Pretraining Data: A Case Study of Pretraining BERT on Social Media
Recent studies on domain-specific BERT models show that effectiveness on downstream tasks can be improved when models are pretrained on in-domain data. Often, the pretraining data used in these models are selected based …
Clinical Concept ExtractionExtracting clinical concepts from user queries
Clinical concept extraction often begins with clinical Named Entity Recognition (NER). Often trained on annotated clinical notes, clinical NER models tend to struggle with tagging clinical entities in user queries becaus…
Clinical Concept Extractionnamed-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)+1Clinical Concept Extraction: a Methodology Review
Background Concept extraction, a subdomain of natural language processing (NLP) with a focus on extracting concepts of interest, has been adopted to computationally extract clinical information from text for a wide range…
ArticlesClinical Concept ExtractionDecision MakingEmbedding Strategies for Specialized Domains: Application to Clinical Entity Recognition
Using pre-trained word embeddings in conjunction with Deep Learning models has become the {``}de facto{''} approach in Natural Language Processing (NLP). While this usually yields satisfactory results, off-the-shelf word…
Clinical Concept ExtractionWord EmbeddingsClinical Concept Extraction for Document-Level Coding
The text of clinical notes can be a valuable source of patient information and clinical assessments. Historically, the primary approach for exploiting clinical notes has been information extraction: linking spans of text…
Clinical Concept ExtractionEnhancing Clinical Concept Extraction with Contextual Embeddings
Neural network-based representations ("embeddings") have dramatically advanced natural language processing (NLP) tasks, including clinical NLP tasks such as concept extraction. Recently, however, more advanced embedding …
Clinical Concept ExtractionLanguage ModellingLarge Language ModelWord EmbeddingsClinical Concept Extraction with Contextual Word Embedding
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 m…
Clinical Concept ExtractionIdentifying Risk Factors For Heart Disease in Electronic Medical Records: A Deep Learning Approach
Automatic identification of heart disease risk factors in clinical narratives can expedite disease progression modelling and support clinical decisions. Existing practical solutions for cardiovascular risk detection are …
Clinical Concept ExtractionDeep LearningFeature Engineeringtext-classification+1CliNER 2.0: Accessible and Accurate Clinical Concept Extraction
Clinical notes often describe important aspects of a patient's stay and are therefore critical to medical research. Clinical concept extraction (CCE) of named entities - such as problems, tests, and treatments - aids in …
Clinical Concept ExtractionDecision Makingnamed-entity-recognitionNamed Entity Recognition+2Recurrent neural networks with specialized word embeddings for health-domain named-entity recognition
Background. Previous state-of-the-art systems on Drug Name Recognition (DNR) and Clinical Concept Extraction (CCE) have focused on a combination of text "feature engineering" and conventional machine learning algorithms …
BIG-bench Machine LearningClinical Concept ExtractionFeature Engineeringnamed-entity-recognition+3Bidirectional LSTM-CRF for Clinical Concept Extraction
Automated extraction of concepts from patient clinical records is an essential facilitator of clinical research. For this reason, the 2010 i2b2/VA Natural Language Processing Challenges for Clinical Records introduced a …
Clinical Concept ExtractionWord Embeddings