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Found 8 papers, 3 papers with code
Does Biomedical Training Lead to Better Medical Performance?
This study investigates the effect of biomedical training in the context of six practical medical tasks evaluating $25$ models.
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.
Ranked #10 on
Zero-Shot Learning
on MedConceptsQA
Conditional GAN for timeseries generation
It is abundantly clear that time dependent data is a vital source of information in the world.
