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Combining Contextualized Embeddings and Prior Knowledge for Clinical Named Entity Recognition: Evaluation Study

https://doi.org/10.2196/14850
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Wilcoxon Signed‐Rank Test
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Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program.
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Overview of the ShARe/CLEF eHealth Evaluation Lab 2013
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Automated systems for the de-identification of longitudinal clinical narratives: Overview of 2014 i2b2/UTHealth shared task Track 1
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Automatic de-identification of electronic medical records using token-level and character-level conditional random fields
inferredcites →https://doi.org/10.1016/j.jbi.2015.06.015openalex-snapshot
Automatic detection of protected health information from clinic narratives
inferredcites →https://doi.org/10.1016/j.jbi.2015.06.029openalex-snapshot
Combining knowledge- and data-driven methods for de-identification of clinical narratives
inferredcites →https://doi.org/10.1016/j.jbi.2015.08.012openalex-snapshot
CRFs based de-identification of medical records
inferredcites →https://doi.org/10.1016/j.jbi.2019.103133openalex-snapshot
Incorporating dictionaries into deep neural networks for the Chinese clinical named entity recognition
inferredcites →https://doi.org/10.1016/s1532-0464(03)00012-1openalex-snapshot
Two biomedical sublanguages: a description based on the theories of Zellig Harris
inferredcites →https://doi.org/10.1038/nature14539openalex-snapshot
Deep learning
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MIMIC-III, a freely accessible critical care database
inferredcites →https://doi.org/10.1093/bioinformatics/btw294openalex-snapshot
DeepMeSH: deep semantic representation for improving large-scale MeSH indexing
inferredcites →https://doi.org/10.1136/amiajnl-2011-000150openalex-snapshot
Machine-learned solutions for three stages of clinical information extraction: the state of the art at i2b2 2010
inferredcites →https://doi.org/10.1136/amiajnl-2011-000163openalex-snapshot
A study of machine-learning-based approaches to extract clinical entities and their assertions from discharge summaries
inferredcites →https://doi.org/10.1136/amiajnl-2011-000203openalex-snapshot
2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text
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An end-to-end system to identify temporal relation in discharge summaries: 2012 i2b2 challenge
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Comprehensive temporal information detection from clinical text: medical events, time, and TLINK identification
inferredcites →https://doi.org/10.1136/amiajnl-2013-001625openalex-snapshot
Combining rules and machine learning for extraction of temporal expressions and events from clinical narratives
inferredcites →https://doi.org/10.1136/amiajnl-2013-001628openalex-snapshot
Evaluating temporal relations in clinical text: 2012 i2b2 Challenge
inferredcites →https://doi.org/10.1136/amiajnl-2013-001635openalex-snapshot
A hybrid system for temporal information extraction from clinical text
inferredcites →https://doi.org/10.1136/jamia.1994.95236146openalex-snapshot
A General Natural-language Text Processor for Clinical Radiology
inferredcites →https://doi.org/10.1136/jamia.2009.001560openalex-snapshot
Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications
inferredcites →https://doi.org/10.1136/jamia.2009.002733openalex-snapshot
An overview of MetaMap: historical perspective and recent advances
inferredcites →https://doi.org/10.1136/jamia.2010.003947openalex-snapshot
Extracting medication information from clinical text
inferredcites →https://doi.org/10.1162/neco.1997.9.8.1735openalex-snapshot
Long Short-Term Memory
inferredcites →https://doi.org/10.1186/1472-6947-13-s1-s1openalex-snapshot
Recognizing clinical entities in hospital discharge summaries using Structural Support Vector Machines with word representation features
inferredcites →https://doi.org/10.1186/1472-6947-6-30openalex-snapshot
Extracting principal diagnosis, co-morbidity and smoking status for asthma research: evaluation of a natural language processing system
inferredcites →https://doi.org/10.1186/s12911-017-0468-7openalex-snapshot
Entity recognition from clinical texts via recurrent neural network
inferredcites →https://doi.org/10.18653/v1/d18-1179openalex-snapshot
Dissecting Contextual Word Embeddings: Architecture and Representation
inferredcites →https://doi.org/10.18653/v1/n18-1202openalex-snapshot
Deep Contextualized Word Representations
inferredcites →https://doi.org/10.18653/v1/p17-1161openalex-snapshot
Semi-supervised sequence tagging with bidirectional language models
inferredcites →https://doi.org/10.2196/medinform.9965openalex-snapshot
Clinical Named Entity Recognition From Chinese Electronic Health Records via Machine Learning Methods
inferredcites →https://doi.org/10.3115/1118149.1118154openalex-snapshot
MPLUS
inferredcites →https://doi.org/10.3115/v1/d14-1194openalex-snapshot
#TagSpace: Semantic Embeddings from Hashtags
inferredcites →https://doi.org/10.3115/v1/p14-2089openalex-snapshot
Improving Lexical Embeddings with Semantic Knowledge
inferredcites →https://doi.org/10.3115/v1/p15-1145openalex-snapshot
Learning Semantic Word Embeddings based on Ordinal Knowledge Constraints
inferredcites →https://doi.org/10.63317/28uaw9zbbezgopenalex-snapshot
Medical Concept Embeddings via Labeled Background Corpora

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Identifying stroke-related quantified evidence from electronic health records in real-world studies
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Crosslingual named entity recognition for clinical de-identification applied to a COVID-19 Italian data set
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A clinical named entity recognition model using pretrained word embedding and deep neural networks
inferred← citeshttps://doi.org/10.1016/j.ijmedinf.2023.105122openalex-snapshot
Clinical named entity recognition and relation extraction using natural language processing of medical free text: A systematic review
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CRISPR-OTE: Prediction of CRISPR On-Target Efficiency Based on Multi-Dimensional Feature Fusion
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Integrating domain knowledge for biomedical text analysis into deep learning: A survey
inferred← citeshttps://doi.org/10.1016/j.jksuci.2021.01.007openalex-snapshot
A comprehensive survey of deep learning in the field of medical imaging and medical natural language processing: Challenges and research directions
inferred← citeshttps://doi.org/10.1016/j.knosys.2020.106649openalex-snapshot
Combining contextualized word representation and sub-document level analysis through Bi-LSTM+CRF architecture for clinical de-identification
inferred← citeshttps://doi.org/10.1016/j.procs.2022.09.122openalex-snapshot
Medical Named Entity Recognition using Surrounding Sequences Matching
inferred← citeshttps://doi.org/10.1055/a-2590-6348openalex-snapshot
TCMSF: A Construction Framework of Traditional Chinese Medicine Syndrome Ancient Book Knowledge Graph
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Predicting Heart Failure Readmission from Clinical Notes Using Deep Learning
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Medical Named Entity Recognition (MedNER): A Deep Learning Model for Recognizing Medical Entities (Drug, Disease) from Scientific Texts
inferred← citeshttps://doi.org/10.1109/icitee49829.2020.9271752openalex-snapshot
Domain-Specific Contextualized Embedding: A Systematic Literature Review
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Well-Behaved Transformer for Chinese Medical NER
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BERT-Driven Automation in Electronic Health Record Management System
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Extracting clinical named entity for pituitary adenomas from Chinese electronic medical records
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A mesterséges intelligencia alkalmazása a nyelvtanulásban
inferred← citeshttps://doi.org/10.18280/ria.350405openalex-snapshot
Leveraging Pre-Trained Contextualized Word Embeddings to Enhance Sentiment Classification of Drug Reviews
inferred← citeshttps://doi.org/10.2196/17637openalex-snapshot
Multi-Level Representation Learning for Chinese Medical Entity Recognition: Model Development and Validation
inferred← citeshttps://doi.org/10.2196/17934openalex-snapshot
Hybrid Deep Learning for Medication-Related Information Extraction From Clinical Texts in French: MedExt Algorithm Development Study
inferred← citeshttps://doi.org/10.2196/preprints.31204openalex-snapshot
Domain Heuristic Fusion of Multi-word Embeddings for Nutrient Value Prediction (Preprint)
inferred← citeshttps://doi.org/10.22215/etd/2022-15230openalex-snapshot
Comparative Evaluation on Effect of ELMo in Combination with Machine Learning, and Ensemble Models
inferred← citeshttps://doi.org/10.26599/tst.2021.9010093openalex-snapshot
KTI-RNN: Recognition of Heart Failure from Clinical Notes
inferred← citeshttps://doi.org/10.3390/ijerph19106159openalex-snapshot
Health Communication through Positive and Solidarity Messages Amid the COVID-19 Pandemic: Automated Content Analysis of Facebook Uses
inferred← citeshttps://doi.org/10.3390/math9161941openalex-snapshot
Domain Heuristic Fusion of Multi-Word Embeddings for Nutrient Value Prediction
inferred← citeshttps://doi.org/10.48550/arxiv.2503.05373openalex-snapshot
Leveraging Semantic Type Dependencies for Clinical Named Entity Recognition
inferred← citeshttps://doi.org/10.5220/0010876100003123openalex-snapshot
TAX-Corpus: Taxonomy based Annotations for Colonoscopy Evaluation
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DeIDNER Model: A Neural Network Named Entity Recognition Model for Use in the De-identification of Clinical Notes
inferred← citeshttps://doi.org/10.5220/0010903300003123openalex-snapshot
The h-ANN Model: Comprehensive Colonoscopy Concept Compilation using Combined Contextual Embeddings
inferred← citeshttps://doi.org/10.70675/4c63eb46zd8cfz4a5dz80e9z666563e756d8openalex-snapshot
Domain adaptation of word embeddings through the exploitation of in-domain corpora and knowledge bases
inferred← citeshttps://doi.org/10.7287/peerj-cs.384v0.1/reviews/1openalex-snapshot
Peer Review #1 of "Comparing general and specialized word embeddings for biomedical named entity recognition (v0.1)"
inferred← citeshttps://doi.org/10.7287/peerj-cs.384v0.1/reviews/2openalex-snapshot
Peer Review #2 of "Comparing general and specialized word embeddings for biomedical named entity recognition (v0.1)"
inferred← citeshttps://doi.org/10.7287/peerj-cs.384v0.1/reviews/3openalex-snapshot
Peer Review #3 of "Comparing general and specialized word embeddings for biomedical named entity recognition (v0.1)"
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Comparing general and specialized word embeddings for biomedical named entity recognition
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