AIMC Topic:
Data Mining

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[Progress in application of machine learning in epidemiology].

Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi
Population based health data collection and analysis are important in epidemiological research. In recent years, with the rapid development of big data, Internet and cloud computing, artificial intelligence has gradually attracted attention of epidem...

Improving dictionary-based named entity recognition with deep learning.

Bioinformatics (Oxford, England)
MOTIVATION: Dictionary-based named entity recognition (NER) allows terms to be detected in a corpus and normalized to biomedical databases and ontologies. However, adaptation to different entity types requires new high-quality dictionaries and associ...

LEAP: LLM instruction-example adaptive prompting framework for biomedical relation extraction.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: To investigate the demonstration in large language models (LLMs) for biomedical relation extraction. This study introduces a framework comprising three types of adaptive tuning methods to assess their impacts and effectiveness.

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...

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...

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...