AIMC Topic: Data Mining

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AI for Extracting Pre-Analytical Variability Data from Biomedical Literature: Feasibility and Validation.

Studies in health technology and informatics
INTRODUCTION: The quality and reproducibility of research results from biological samples are significantly influenced by the pre-analytical variability resulting from different conditions during sample collection, storage and processing. Although nu...

Using Data Mining to Differentiate Dengue with Warning Signs from Severe Dengue: A Predictive Model from Oaxaca, Mexico.

The American journal of tropical medicine and hygiene
Dengue with warning signs (DWS) and severe dengue are significant public health concerns in tropical and subtropical regions globally. Accurate and timely differentiation between these clinical forms of dengue, although crucial, is often complex. In ...

Predicting CircRNA-Disease Associations Based on Heterogeneous Graph Neural Network and Knowledge Graph Attribute Mining Attention.

Interdisciplinary sciences, computational life sciences
The exploration of associations between circular RNAs (circRNAs) and diseases contributes to a deeper understanding of the pathogenesis of diseases. Many computational methods have been proposed for circRNA-disease associations identification. Howeve...

Efficient Training Corpus Retrieval for Large Language Model Fine Tuning: A Case Study in Cancer.

Studies in health technology and informatics
The objective is to create an automated knowledge extraction tool for cancer research that builds high-quality academic corpora for LLM fine-tuning while investigating its effectiveness in interleukin-6 and bladder cancer domains. To address the curr...

A Hybrid Natural Language Processing Platform for Multi-Site RWD Studies.

Studies in health technology and informatics
Real-world data (RWD) obtained from electronic medical records has become a valuable resource for healthcare research. However, integrating unstructured free-text clinical data remains a significant challenge. Although natural language processing (NL...

Enhancing Vaccine Safety Surveillance: Extracting Vaccine Mentions from Emergency Department Triage Notes Using Fine-Tuned Large Language Models.

Studies in health technology and informatics
This study evaluates fine-tuned Llama 3.2 models for extracting vaccine-related information from emergency department triage notes to support near real-time vaccine safety surveillance. Prompt engineering was used to initially create a labeled datase...

A Performance-Based Voting Framework for Assertion Detection in Clinical Notes.

Studies in health technology and informatics
Extracting structured information from unstructured clinical text remains a critical challenge in healthcare. This study introduces a robust framework for clinical assertion detection, integrating domain-specific embeddings like BioBERT, contextualiz...

From Text to Knowledge: An End-To-End Extraction Pipeline for Clinical Information.

Studies in health technology and informatics
This study explores the use of Large Language Models (LLMs) in extracting and structuring allergic reaction data from non-English clinical free texts. Leveraging open-source models such as Llama 3.1, Qwen 2.5, and Mistral NeMo, the study utilizes 500...

Structured LLM Augmentation for Clinical Information Extraction.

Studies in health technology and informatics
Information extraction tasks, such as Named Entity Recognition (NER) and Relation Extraction (RE), are essential for advancing clinical research and applications. However, these tasks are hindered by the scarcity of labeled clinical documents due to ...