[HTML][HTML] An empirical evaluation of prompting strategies for large language models in zero-shot clinical natural language processing: algorithm development and …
S Sivarajkumar, M Kelley… - JMIR Medical …, 2024 - medinform.jmir.org
Background Large language models (LLMs) have shown remarkable capabilities in natural
language processing (NLP), especially in domains where labeled data are scarce or …
language processing (NLP), especially in domains where labeled data are scarce or …
Large language models in biomedicine and health: current research landscape and future directions
Large language models (LLMs) are a specialized type of generative artificial intelligence (AI)
focused on generating natural language text. These models are developed through …
focused on generating natural language text. These models are developed through …
Harnessing EHR data for health research
AS Tang, SR Woldemariam, S Miramontes… - Nature Medicine, 2024 - nature.com
With the increasing availability of rich, longitudinal, real-world clinical data recorded in
electronic health records (EHRs) for millions of patients, there is a growing interest in …
electronic health records (EHRs) for millions of patients, there is a growing interest in …
Me llama: Foundation large language models for medical applications
Recent large language models (LLMs) like ChatGPT and LLaMA have shown great promise
in many AI applications. However, their performance on medical tasks is suboptimal and can …
in many AI applications. However, their performance on medical tasks is suboptimal and can …
Advancing entity recognition in biomedicine via instruction tuning of large language models
Abstract Motivation Large Language Models (LLMs) have the potential to revolutionize the
field of Natural Language Processing, excelling not only in text generation and reasoning …
field of Natural Language Processing, excelling not only in text generation and reasoning …
Zero-shot evaluation of ChatGPT for food named-entity recognition and linking
Introduction Recognizing and extracting key information from textual data plays an important
role in intelligent systems by maintaining up-to-date knowledge, reinforcing informed …
role in intelligent systems by maintaining up-to-date knowledge, reinforcing informed …
Investigating the increase of violent speech in Incel communities with human-guided GPT-4 prompt iteration
D Matter, M Schirmer, N Grinberg… - Frontiers in Social …, 2024 - frontiersin.org
This study investigates the prevalence of violent language on incels. is. It evaluates GPT
models (GPT-3.5 and GPT-4) for content analysis in social sciences, focusing on the impact …
models (GPT-3.5 and GPT-4) for content analysis in social sciences, focusing on the impact …
Utilizing active learning strategies in machine-assisted annotation for clinical named entity recognition: a comprehensive analysis considering annotation costs and …
J Liu, ZSY Wong - Journal of the American Medical Informatics …, 2024 - academic.oup.com
Objectives Active learning (AL) has rarely integrated diversity-based and uncertainty-based
strategies into a dynamic sampling framework for clinical named entity recognition (NER) …
strategies into a dynamic sampling framework for clinical named entity recognition (NER) …
A comparative study of large language model-based zero-shot inference and task-specific supervised classification of breast cancer pathology reports
Objective Although supervised machine learning is popular for information extraction from
clinical notes, creating large annotated datasets requires extensive domain expertise and is …
clinical notes, creating large annotated datasets requires extensive domain expertise and is …
[HTML][HTML] Applying generative AI with retrieval augmented generation to summarize and extract key clinical information from electronic health records
Background Malnutrition is a prevalent issue in aged care facilities (RACFs), leading to
adverse health outcomes. The ability to efficiently extract key clinical information from a large …
adverse health outcomes. The ability to efficiently extract key clinical information from a large …