AIMC Topic: Data Mining

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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...

Zero-Shot LLMs for Named Entity Recognition: Targeting Cardiac Function Indicators in German Clinical Texts.

Studies in health technology and informatics
INTRODUCTION: Large Language Models (LLMs) like ChatGPT have become increasingly prevalent. In medicine, many potential areas arise where LLMs may offer added value. Our research focuses on the use of open-source LLM alternatives like Llama 3, Gemma,...

Extending the TOP Framework with an Ontology-Based Text Search Component.

Studies in health technology and informatics
INTRODUCTION: Constructing search queries that deal with complex concepts is a challenging task without proficiency in the underlying query language - which holds true for either structured or unstructured data. Medical data might encompass both type...

Integrating deep learning architectures for enhanced biomedical relation extraction: a pipeline approach.

Database : the journal of biological databases and curation
Biomedical relation extraction from scientific publications is a key task in biomedical natural language processing (NLP) and can facilitate the creation of large knowledge bases, enable more efficient knowledge discovery, and accelerate evidence syn...

Using Retrieval-Augmented Generation to Capture Molecularly-Driven Treatment Relationships for Precision Oncology.

Studies in health technology and informatics
Modern generative artificial intelligence techniques like retrieval-augmented generation (RAG) may be applied in support of precision oncology treatment discussions. Experts routinely review published literature for evidence and recommendations of tr...

Optimizing Data Extraction: Harnessing RAG and LLMs for German Medical Documents.

Studies in health technology and informatics
In the field of medical data analysis, converting unstructured text documents into a structured format suitable for further use is a significant challenge. This study introduces an automated local deployed data privacy secure pipeline that uses open-...

Unveiling Medical Insights: Advanced Topic Extraction from Scientific Articles.

Studies in health technology and informatics
In the ever-evolving landscape of medical research and healthcare, the abundance of scientific articles presents both a treasure trove of knowledge and a daunting challenge. Researchers, clinicians, and data scientists grapple with vast amounts of un...

A Comprehensive Natural Language Processing Pipeline for the Chronic Lupus Disease.

Studies in health technology and informatics
Electronic Health Records (EHRs) contain a wealth of unstructured patient data, making it challenging for physicians to do informed decisions. In this paper, we introduce a Natural Language Processing (NLP) approach for the extraction of therapies, d...

Exploring Offline Large Language Models for Clinical Information Extraction: A Study of Renal Histopathological Reports of Lupus Nephritis Patients.

Studies in health technology and informatics
Open source, lightweight and offline generative large language models (LLMs) hold promise for clinical information extraction due to their suitability to operate in secured environments using commodity hardware without token cost. By creating a simpl...

Enhancing Clinical Data Extraction from Pathology Reports: A Comparative Analysis of Large Language Models.

Studies in health technology and informatics
This study evaluates the efficacy of a small large language model (sLLM) in extracting critical information from free-text pathology reports across multiple centers, addressing the challenges posed by the narrative and complex nature of these documen...