AI Medical Compendium Journal:
Journal of the American Medical Informatics Association : JAMIA

Showing 141 to 150 of 493 articles

Ensemble pretrained language models to extract biomedical knowledge from literature.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: The rapid expansion of biomedical literature necessitates automated techniques to discern relationships between biomedical concepts from extensive free text. Such techniques facilitate the development of detailed knowledge bases and highl...

A comparative analysis of Spanish Clinical encoder-based models on NER and classification tasks.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: This comparative analysis aims to assess the efficacy of encoder Language Models for clinical tasks in the Spanish language. The primary goal is to identify the most effective resources within this context.

Large language models and generative AI in telehealth: a responsible use lens.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: This scoping review aims to assess the current research landscape of the application and use of large language models (LLMs) and generative Artificial Intelligence (AI), through tools such as ChatGPT in telehealth. Additionally, the review...

Taiyi: a bilingual fine-tuned large language model for diverse biomedical tasks.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Most existing fine-tuned biomedical large language models (LLMs) focus on enhancing performance in monolingual biomedical question answering and conversation tasks. To investigate the effectiveness of the fine-tuned LLMs on diverse biomedi...

BioLORD-2023: semantic textual representations fusing large language models and clinical knowledge graph insights.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: In this study, we investigate the potential of large language models (LLMs) to complement biomedical knowledge graphs in the training of semantic models for the biomedical and clinical domains.

Clinical risk prediction using language models: benefits and considerations.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: The use of electronic health records (EHRs) for clinical risk prediction is on the rise. However, in many practical settings, the limited availability of task-specific EHR data can restrict the application of standard machine learning pipe...

Search still matters: information retrieval in the era of generative AI.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Information retrieval (IR, also known as search) systems are ubiquitous in modern times. How does the emergence of generative artificial intelligence (AI), based on large language models (LLMs), fit into the IR process?

Improving large language models for clinical named entity recognition via prompt engineering.

Journal of the American Medical Informatics Association : JAMIA
IMPORTANCE: The study highlights the potential of large language models, specifically GPT-3.5 and GPT-4, in processing complex clinical data and extracting meaningful information with minimal training data. By developing and refining prompt-based str...

Automatic uncovering of patient primary concerns in portal messages using a fusion framework of pretrained language models.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: The surge in patient portal messages (PPMs) with increasing needs and workloads for efficient PPM triage in healthcare settings has spurred the exploration of AI-driven solutions to streamline the healthcare workflow processes, ensuring t...

Stratifying heart failure patients with graph neural network and transformer using Electronic Health Records to optimize drug response prediction.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: Heart failure (HF) impacts millions of patients worldwide, yet the variability in treatment responses remains a major challenge for healthcare professionals. The current treatment strategies, largely derived from population based evidence...