.png){kind=icon}
.png){kind=icon}
Return
Medication information extraction using local large language models
.png){kind=icon}
OA
AI
DOI：10.1016/j.jbi.2025.104898.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Medication information is crucial for clinical routine and research. However, a vast amount is stored in unstructured text, such as doctor’s letters, requiring manual extraction – a resource-intensive, error-prone task. Automating this process comes with significant constraints in a clinical setup, including the demand for clinical expertise, limited time-resources, restricted IT infrastructure, and the demand for transparent predictions. Recent advances in generative large language models (LLMs) and parameter-efficient fine-tuning methods show potential to address these challenges.
Keywords:
Medication information extraction
Large language models
Llama
Clinical NLP
Fine-tuning
Interpretability
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
4.5
Papers:
3.5K
Citations:
1.9W
Organization
View more organizations