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Found 11 papers, 2 papers with code
Me LLaMA: Foundation Large Language Models for Medical Applications
In response to this challenge, this study introduces Me-LLaMA, a novel medical LLM family that includes foundation models - Me-LLaMA 13/70B, along with their chat-enhanced versions - Me-LLaMA 13/70B-chat, developed through continual pre-training and instruction tuning of LLaMA2 using large medical datasets.
Feasibility of Identifying Factors Related to Alzheimer's Disease and Related Dementia in Real-World Data
We constructed an interactive knowledge map to disseminate our study results.
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.
Identifying Symptoms of Delirium from Clinical Narratives Using Natural Language Processing
To assist in the diagnosis and phenotyping of delirium, we formed an expert panel to categorize diverse delirium symptoms, composed annotation guidelines, created a delirium corpus with diverse delirium symptoms, and developed NLP methods to extract delirium symptoms from clinical notes.
Contextualized Medication Information Extraction Using Transformer-based Deep Learning Architectures
Materials and methods: We developed NLP systems for medication mention extraction, event classification (indicating medication changes discussed or not), and context classification to classify medication changes context into 5 orthogonal dimensions related to drug changes.
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.
Online Binary Models are Promising for Distinguishing Temporally Consistent Computer Usage Profiles
This paper investigates whether computer usage profiles comprised of process-, network-, mouse-, and keystroke-related events are unique and consistent over time in a naturalistic setting, discussing challenges and opportunities of using such profiles in applications of continuous authentication.
Learning Fast and Slow: PROPEDEUTICA for Real-time Malware Detection
Existing malware detectors on safety-critical devices have difficulties in runtime detection due to the performance overhead.
