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Found 8 papers, 3 papers with code
CLUE: A Clinical Language Understanding Evaluation for LLMs
CLUE represents a step towards a standardized approach to evaluating and developing LLMs in healthcare to align future model development with the real-world needs of clinical application.
Improving Generalizability of Extracting Social Determinants of Health Using Large Language Models through Prompt-tuning
The progress in natural language processing (NLP) using large language models (LLMs) has greatly improved patient information extraction from clinical narratives.
Generative Large Language Models Are All-purpose Text Analytics Engines: Text-to-text Learning Is All Your Need
Objective To solve major clinical natural language processing (NLP) tasks using a unified text-to-text learning architecture based on a generative large language model (LLM) via prompt tuning.
On the Impact of Cross-Domain Data on German Language Models
Traditionally, large language models have been either trained on general web crawls or domain-specific data.
Model Tuning or Prompt Tuning? A Study of Large Language Models for Clinical Concept and Relation Extraction
We evaluated the transfer learning ability of the prompt-based learning algorithms in a cross-institution setting.
A Study of Generative Large Language Model for Medical Research and Healthcare
This study provides insights on the opportunities and challenges of LLMs for medical research and healthcare.
GatorTron: A Large Clinical Language Model to Unlock Patient Information from Unstructured Electronic Health Records
GatorTron models scale up the clinical language model from 110 million to 8. 9 billion parameters and improve 5 clinical NLP tasks (e. g., 9. 6% and 9. 5% improvement in accuracy for NLI and MQA), which can be applied to medical AI systems to improve healthcare delivery.
Conditional GAN for timeseries generation
It is abundantly clear that time dependent data is a vital source of information in the world.
