AIMC Topic: Natural Language Processing

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Investigation of cell development and tissue structure network based on natural Language processing of scRNA-seq data.

Journal of translational medicine
BACKGROUND: Single-cell multi-omics technologies, particularly single-cell RNA sequencing (scRNA-seq), have revolutionized our understanding of cellular heterogeneity and development by providing insights into gene expression at the single-cell level...

Vision-language large learning model, GPT4V, accurately classifies the Boston Bowel Preparation Scale score.

BMJ open gastroenterology
INTRODUCTION: Large learning models (LLMs) such as GPT are advanced artificial intelligence (AI) models. Originally developed for natural language processing, they have been adapted for multi-modal tasks with vision-language input. One clinically rel...

Using a Longformer Large Language Model for Segmenting Unstructured Cancer Pathology Reports.

JCO clinical cancer informatics
PURPOSE: Many Natural Language Processing (NLP) methods achieve greater performance when the input text is preprocessed to remove extraneous or unnecessary text. A technique known as text segmentation can facilitate this step by isolating key section...

An NLP-based method to mine gene and function relationships from published articles.

Scientific reports
Understanding the intricacies of genes function within biological systems is paramount for scientific advancement and medical progress. Owing to the evolving landscape of this research and the complexity of biological processes, however, this task pr...

AI-powered topic modeling: comparing LDA and BERTopic in analyzing opioid-related cardiovascular risks in women.

Experimental biology and medicine (Maywood, N.J.)
Topic modeling is a crucial technique in natural language processing (NLP), enabling the extraction of latent themes from large text corpora. Traditional topic modeling, such as Latent Dirichlet Allocation (LDA), faces limitations in capturing the se...

Integrating language into medical visual recognition and reasoning: A survey.

Medical image analysis
Vision-Language Models (VLMs) are regarded as efficient paradigms that build a bridge between visual perception and textual interpretation. For medical visual tasks, they can benefit from expert observation and physician knowledge extracted from text...

Beyond Borders, Beneath Words: Using Natural Language Processing to Map Student Reflections on International Service Learning to Physical Therapy Values.

Journal, physical therapy education
INTRODUCTION: Advances in artificial intelligence (AI) offer physical therapy educators opportunities to improve student learning. We applied AI to identify themes and determine the extent to which physical therapy professional values were reflected ...

Using Machine Learning to Improve Readmission Risk in Surgical Patients in South Africa.

International journal of environmental research and public health
Unplanned readmission within 30 days is a major challenge both globally and in South Africa. The aim of this study was to develop a machine learning model to predict unplanned surgical and trauma readmission to a public hospital in South Africa from ...

Enhancing biomedical named entity recognition with parallel boundary detection and category classification.

BMC bioinformatics
BACKGROUND: Named entity recognition is a fundamental task in natural language processing. Recognizing entities in biomedical text, known as the BioNER, is particularly crucial for cutting-edge applications. However, BioNER poses greater challenges c...