Privacy and ethical considerations limit access to large-scale clinical datasets, particularly
clinical text data, which contain extensive and diverse information and serve as the …

Objective We develop a deep learning framework based on the pre-trained Bidirectional
Encoder Representations from Transformers (BERT) model using unstructured clinical notes …

Summary Background Large language models (LLMs) have shown promising performance
in various healthcare domains, but their effectiveness in identifying specific clinical …

Large language models (LLMs) can generate natural language texts for various domains
and tasks, but their potential for clinical text mining, a domain with scarce, sensitive, and …

Background Geriatric emergency department (GED) guidelines endorse screening older
patients for geriatric syndromes in the ED, but there have been significant barriers to …

Background and aim Neurodegenerative disorders (eg, Alzheimer's, Parkinson's) lead to
neuronal loss; neurocognitive disorders (eg, delirium, dementia) show cognitive decline …

Background This study aims to explore a deep learning (DL) algorithm for developing a
prognostic model and perform survival analyses in SBT patients. Methods The demographic …

Over 78 million people will suffer from dementia by 2030, emphasizing the need for early
identification of patients with mild cognitive impairment (MCI) at risk, and personalized …

This study assesses the ability of state-of-the-art large language models (LLMs) including
GPT-3.5, GPT-4, Falcon, and LLaMA 2 to identify patients with mild cognitive impairment …

Objective: We develop a deep learning framework based on the pre-trained Bidirectional
Encoder Representations from Transformers (BERT) model using unstructured clinical notes …