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Found 31 papers, 2 papers with code
Temporal Annotation in the Clinical Domain
The corpus is available to the community and has been proposed for use in a SemEval 2015 task.
SemEval-2017 Task 12: Clinical TempEval
Clinical TempEval 2017 aimed to answer the question: how well do systems trained on annotated timelines for one medical condition (colon cancer) perform in predicting timelines on another medical condition (brain cancer)?
Representations of Time Expressions for Temporal Relation Extraction with Convolutional Neural Networks
Token sequences are often used as the input for Convolutional Neural Networks (CNNs) in natural language processing.
Unsupervised Domain Adaptation for Clinical Negation Detection
Detecting negated concepts in clinical texts is an important part of NLP information extraction systems.
Repeat before Forgetting: Spaced Repetition for Efficient and Effective Training of Neural Networks
We present a novel approach for training artificial neural networks.
Spotting Spurious Data with Neural Networks
Automatic identification of spurious instances (those with potentially wrong labels in datasets) can improve the quality of existing language resources, especially when annotations are obtained through crowdsourcing or automatically generated based on coded rankings.
Self-training improves Recurrent Neural Networks performance for Temporal Relation Extraction
Neural network models are oftentimes restricted by limited labeled instances and resort to advanced architectures and features for cutting edge performance.
Cross-document coreference: An approach to capturing coreference without context
This paper discusses a cross-document coreference annotation schema that was developed to further automatic extraction of timelines in the clinical domain.
Diagnosis Prevalence vs. Efficacy in Machine-learning Based Diagnostic Decision Support
Scores fell from 0. 28 for the 50 most prevalent ICD-9-CM codes to 0. 03 for the 1000 most prevalent ICD-9-CM codes.
Mining Misdiagnosis Patterns from Biomedical Literature
Diagnostic errors can pose a serious threat to patient safety, leading to serious harm and even death.
A BERT-based One-Pass Multi-Task Model for Clinical Temporal Relation Extraction
Recently BERT has achieved a state-of-the-art performance in temporal relation extraction from clinical Electronic Medical Records text.
Measuring Pointwise $\mathcal{V}$-Usable Information In-Context-ly
In-context learning (ICL) is a new learning paradigm that has gained popularity along with the development of large language models.
EntityBERT: Entity-centric Masking Strategy for Model Pretraining for the Clinical Domain
We propose a methodology to produce a model focused on the clinical domain: continued pretraining of a model with a broad representation of biomedical terminology (PubMedBERT) on a clinical corpus along with a novel entity-centric masking strategy to infuse domain knowledge in the learning process.
Defining and Learning Refined Temporal Relations in the Clinical Narrative
We present refinements over existing temporal relation annotations in the Electronic Medical Record clinical narrative.
Extracting Relations between Radiotherapy Treatment Details
We present work on extraction of radiotherapy treatment information from the clinical narrative in the electronic medical records.
Exploring Text Representations for Generative Temporal Relation Extraction
Sequence-to-sequence models are appealing because they allow both encoder and decoder to be shared across many tasks by formulating those tasks as text-to-text problems.
Ensemble-based Fine-Tuning Strategy for Temporal Relation Extraction from the Clinical Narrative
In this paper, we investigate ensemble methods for fine-tuning transformer-based pretrained models for clinical natural language processing tasks, specifically temporal relation extraction from the clinical narrative.
