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Found 11 papers, 8 papers with code
Assertion Detection in Clinical Notes: Medical Language Models to the Rescue?
In order to provide high-quality care, health professionals must efficiently identify the presence, possibility, or absence of symptoms, treatments and other relevant entities in free-text clinical notes.
Surveying (Dis)Parities and Concerns of Compute Hungry NLP Research
Many recent improvements in NLP stem from the development and use of large pre-trained language models (PLMs) with billions of parameters.
This Patient Looks Like That Patient: Prototypical Networks for Interpretable Diagnosis Prediction from Clinical Text
The use of deep neural models for diagnosis prediction from clinical text has shown promising results.
Efficient Methods for Natural Language Processing: A Survey
Recent work in natural language processing (NLP) has yielded appealing results from scaling model parameters and training data; however, using only scale to improve performance means that resource consumption also grows.
Cross-Lingual Knowledge Transfer for Clinical Phenotyping
Clinical phenotyping enables the automatic extraction of clinical conditions from patient records, which can be beneficial to doctors and clinics worldwide.
What Do You See in this Patient? Behavioral Testing of Clinical NLP Models
We thus introduce an extendable testing framework that evaluates the behavior of clinical outcome models regarding changes of the input.
Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge Integration
Outcome prediction from clinical text can prevent doctors from overlooking possible risks and help hospitals to plan capacities.
Ranked #1 on
Medical Diagnosis
on Clinical Admission Notes from MIMIC-III
(using extra training data)
VisBERT: Hidden-State Visualizations for Transformers
At the same time, they are difficult to incorporate into the large, black-box models that achieve state-of-the-art results in a multitude of NLP tasks.
Learning Contextualized Document Representations for Healthcare Answer Retrieval
Our model leverages a dual encoder architecture with hierarchical LSTM layers and multi-task training to encode the position of clinical entities and aspects alongside the document discourse.
How Does BERT Answer Questions? A Layer-Wise Analysis of Transformer Representations
In order to better understand BERT and other Transformer-based models, we present a layer-wise analysis of BERT's hidden states.
Challenges for Toxic Comment Classification: An In-Depth Error Analysis
Toxic comment classification has become an active research field with many recently proposed approaches.
