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Relation Classification for Bleeding Events From Electronic Health Records Using Deep Learning Systems: An Empirical Study
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/pds.4850openalex-snapshot
Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program.
inferredcites →https://doi.org/10.1007/978-3-642-53917-6_21openalex-snapshot
Convolution Neural Network for Relation Extraction
inferredcites →https://doi.org/10.1007/s40264-018-0762-zopenalex-snapshot
Overview of the First Natural Language Processing Challenge for Extracting Medication, Indication, and Adverse Drug Events from Electronic Health Record Notes (MADE 1.0)
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Patterns and Predictors of Warfarin Use in Patients With New-Onset Atrial Fibrillation from the FRACTAL Registry
inferredcites →https://doi.org/10.1016/j.amjcard.2013.05.063openalex-snapshot
Estimates of Current and Future Incidence and Prevalence of Atrial Fibrillation in the U.S. Adult Population
inferredcites →https://doi.org/10.1016/j.artmed.2018.05.001openalex-snapshot
Classifying medical relations in clinical text via convolutional neural networks
inferredcites →https://doi.org/10.1016/j.ins.2020.10.047openalex-snapshot
A novel joint biomedical event extraction framework via two-level modeling of documents
inferredcites →https://doi.org/10.1016/j.jacc.2006.10.062openalex-snapshot
Mortality Trends in Patients Diagnosed With First Atrial Fibrillation
inferredcites →https://doi.org/10.1016/j.jbi.2012.04.008openalex-snapshot
Development of a benchmark corpus to support the automatic extraction of drug-related adverse effects from medical case reports
inferredcites →https://doi.org/10.1016/j.jbi.2015.08.013openalex-snapshot
Identifying adverse drug event information in clinical notes with distributional semantic representations of context
inferredcites →https://doi.org/10.1016/s0140-6736(07)61233-1openalex-snapshot
Warfarin versus aspirin for stroke prevention in an elderly community population with atrial fibrillation (the Birmingham Atrial Fibrillation Treatment of the Aged Study, BAFTA): a randomised controlled trial
inferredcites →https://doi.org/10.1038/s42256-020-00257-zopenalex-snapshot
Shortcut learning in deep neural networks
inferredcites →https://doi.org/10.1093/bioinformatics/btz682openalex-snapshot
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
inferredcites →https://doi.org/10.1093/database/baw036openalex-snapshot
CD-REST: a system for extracting chemical-induced disease relation in literature
inferredcites →https://doi.org/10.1093/jamia/ocaa136openalex-snapshot
Use of word and graph embedding to measure semantic relatedness between Unified Medical Language System concepts
inferredcites →https://doi.org/10.1093/jamia/ocx090openalex-snapshot
Segment convolutional neural networks (Seg-CNNs) for classifying relations in clinical notes
inferredcites →https://doi.org/10.1093/jamia/ocz018openalex-snapshot
An investigation of single-domain and multidomain medication and adverse drug event relation extraction from electronic health record notes using advanced deep learning models
inferredcites →https://doi.org/10.1093/jamia/ocz101openalex-snapshot
Adverse drug events and medication relation extraction in electronic health records with ensemble deep learning methods
inferredcites →https://doi.org/10.1093/jamia/ocz166openalex-snapshot
2018 n2c2 shared task on adverse drug events and medication extraction in electronic health records
inferredcites →https://doi.org/10.1093/nar/gkh061openalex-snapshot
The Unified Medical Language System (UMLS): integrating biomedical terminology
inferredcites →https://doi.org/10.1109/cvpr.2017.243openalex-snapshot
Densely Connected Convolutional Networks
inferredcites →https://doi.org/10.1136/amiajnl-2011-000203openalex-snapshot
2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text
inferredcites →https://doi.org/10.1136/bmj.d3653openalex-snapshot
Performance of stroke risk scores in older people with atrial fibrillation not taking warfarin: comparative cohort study from BAFTA trial
inferredcites →https://doi.org/10.1145/3357384.3358119openalex-snapshot
Enriching Pre-trained Language Model with Entity Information for Relation Classification
inferredcites →https://doi.org/10.1161/01.str.22.8.983openalex-snapshot
Atrial fibrillation as an independent risk factor for stroke: the Framingham Study.
inferredcites →https://doi.org/10.1161/circulationaha.106.653048openalex-snapshot
Major Hemorrhage and Tolerability of Warfarin in the First Year of Therapy Among Elderly Patients With Atrial Fibrillation
inferredcites →https://doi.org/10.1161/circulationaha.113.006426openalex-snapshot
Periprocedural Stroke and Bleeding Complications in Patients Undergoing Catheter Ablation of Atrial Fibrillation With Different Anticoagulation Management
inferredcites →https://doi.org/10.1186/1471-2105-15-64openalex-snapshot
Knowledge-based extraction of adverse drug events from biomedical text
inferredcites →https://doi.org/10.1197/jamia.m1552openalex-snapshot
Automated Encoding of Clinical Documents Based on Natural Language Processing
inferredcites →https://doi.org/10.1197/jamia.m2777openalex-snapshot
Comparison of Information Content of Structured and Narrative Text Data Sources on the Example of Medication Intensification
inferredcites →https://doi.org/10.1378/chest.13-1635openalex-snapshot
Development of a Novel Composite Stroke and Bleeding Risk Score in Patients With Atrial Fibrillation
inferredcites →https://doi.org/10.18653/v1/2020.emnlp-demos.6openalex-snapshot
Transformers: State-of-the-Art Natural Language Processing
inferredcites →https://doi.org/10.18653/v1/d15-1062openalex-snapshot
Semantic Relation Classification via Convolutional Neural Networks with Simple Negative Sampling
inferredcites →https://doi.org/10.18653/v1/d18-1244openalex-snapshot
Graph Convolution over Pruned Dependency Trees Improves Relation Extraction
inferredcites →https://doi.org/10.18653/v1/d19-1503openalex-snapshot
Learning to Infer Entities, Properties and their Relations from Clinical Conversations
inferredcites →https://doi.org/10.18653/v1/n19-1423openalex-snapshot
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
inferredcites →https://doi.org/10.18653/v1/p16-1123openalex-snapshot
Relation Classification via Multi-Level Attention CNNs
inferredcites →https://doi.org/10.18653/v1/p19-1024openalex-snapshot
Attention Guided Graph Convolutional Networks for Relation Extraction
inferredcites →https://doi.org/10.18653/v1/p19-1279openalex-snapshot
Matching the Blanks: Distributional Similarity for Relation Learning
inferredcites →https://doi.org/10.2196/12159openalex-snapshot
Extraction of Information Related to Adverse Drug Events from Electronic Health Record Notes: Design of an End-to-End Model Based on Deep Learning
inferredcites →https://doi.org/10.2196/14830openalex-snapshot
Fine-Tuning Bidirectional Encoder Representations From Transformers (BERT)–Based Models on Large-Scale Electronic Health Record Notes: An Empirical Study
inferredcites →https://doi.org/10.2196/publichealth.9361openalex-snapshot
Clinical Relation Extraction Toward Drug Safety Surveillance Using Electronic Health Record Narratives: Classical Learning Versus Deep Learning
inferredcites →https://doi.org/10.3115/v1/d14-1162openalex-snapshot
Glove: Global Vectors for Word Representation
inferredcites →https://doi.org/10.3115/v1/w15-1506openalex-snapshot
Relation Extraction: Perspective from Convolutional Neural Networks
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1016/j.inffus.2025.103024openalex-snapshot
A review of medical text analysis: Theory and practice
inferred← citeshttps://doi.org/10.1055/a-2121-8380openalex-snapshot
Doctors Identify Hemorrhage Better during Chart Review when Assisted by Artificial Intelligence
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Using Data Science to Improve Outcomes for Persons with Opioid use Disorder
inferred← citeshttps://doi.org/10.1093/jamia/ocad190openalex-snapshot
Improving model transferability for clinical note section classification models using continued pretraining
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Transformer-Based Cardiovascular Event Prediction Using National Claims Data
inferred← citeshttps://doi.org/10.1109/iccect57938.2023.10140959openalex-snapshot
RDRS: Represent Document-level Relation with Sentence-level Relation by Distant Supervision
inferred← citeshttps://doi.org/10.1161/circep.124.013023openalex-snapshot
Engineering of Generative Artificial Intelligence and Natural Language Processing Models to Accurately Identify Arrhythmia Recurrence
inferred← citeshttps://doi.org/10.1177/14604582251315594openalex-snapshot
Identifying protected health information by transformers-based deep learning approach in Chinese medical text
inferred← citeshttps://doi.org/10.1186/s12911-022-01977-5openalex-snapshot
BertSRC: transformer-based semantic relation classification
inferred← citeshttps://doi.org/10.1186/s12967-023-04011-yopenalex-snapshot
Precision information extraction for rare disease epidemiology at scale
inferred← citeshttps://doi.org/10.1371/journal.pone.0329963openalex-snapshot
Automated classification of clinical diagnoses in electronic health records using transformer
inferred← citeshttps://doi.org/10.1590/s2175-97902026e24622openalex-snapshot
Triggers of drug-related bleeding events: a systematic review
inferred← citeshttps://doi.org/10.17762/ijfrcsce.v8i3.2091openalex-snapshot
Computational Intelligence Based Electronic Healthcare Data Analytics Using Feature Selection with Classification by Deep Learning Architecture
inferred← citeshttps://doi.org/10.18653/v1/2023.eacl-main.86openalex-snapshot
What Clued the AI Doctor In? On the Influence of Data Source and Quality for Transformer-Based Medical Self-Disclosure Detection
inferred← citeshttps://doi.org/10.18653/v1/2023.findings-eacl.103openalex-snapshot
Investigating anatomical bias in clinical machine learning algorithms
inferred← citeshttps://doi.org/10.2196/44892openalex-snapshot
A Multilabel Text Classifier of Cancer Literature at the Publication Level: Methods Study of Medical Text Classification
inferred← citeshttps://doi.org/10.2196/preprints.110004openalex-snapshot
A Screen-and-Reason Cascade to Extract Bleeding Status From EHR Clinical Notes: Algorithm Development and Validation Study (Preprint)
inferred← citeshttps://doi.org/10.2196/preprints.45728openalex-snapshot
Identifying Protected Health Information by Transformers-Based Deep Learning Approach in Chinese Medical Text (Preprint)
inferred← citeshttps://doi.org/10.23919/wac55640.2022.9934127openalex-snapshot
Research on music information retrieval algorithm based on deep learning
inferred← citeshttps://doi.org/10.3389/fpsyt.2022.861930openalex-snapshot
Identification and Impact Analysis of Family History of Psychiatric Disorder in Mood Disorder Patients With Pretrained Language Model
inferred← citeshttps://doi.org/10.3390/app15168785openalex-snapshot
A Hybrid Approach to Literature-Based Discovery: Combining Traditional Methods with LLMs
inferred← citeshttps://doi.org/10.3390/ijerph19105959openalex-snapshot
A Deep Learning Approach to Estimate the Incidence of Infectious Disease Cases for Routinely Collected Ambulatory Records: The Example of Varicella-Zoster
inferred← citeshttps://doi.org/10.6084/m9.figshare.26571859openalex-snapshot
Additional file 1 of Precision information extraction for rare disease epidemiology at scale
inferred← citeshttps://doi.org/10.6084/m9.figshare.26571862openalex-snapshot
Additional file 2 of Precision information extraction for rare disease epidemiology at scale
inferred← citeshttps://doi.org/10.6084/m9.figshare.26571865openalex-snapshot
Additional file 3 of Precision information extraction for rare disease epidemiology at scale
inferred← citeshttps://doi.org/10.7759/cureus.117173openalex-snapshot
Medical Applications of Graph Convolutional Networks Using Electronic Health Records: A Scoping Review
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