GENIE: Generative Note Information Extraction model for structuring EHR data
Journal:
arXiv
Published Date:
Jan 30, 2025
Abstract
Electronic Health Records (EHRs) hold immense potential for advancing
healthcare, offering rich, longitudinal data that combines structured
information with valuable insights from unstructured clinical notes. However,
the unstructured nature of clinical text poses significant challenges for
secondary applications. Traditional methods for structuring EHR free-text data,
such as rule-based systems and multi-stage pipelines, are often limited by
their time-consuming configurations and inability to adapt across clinical
notes from diverse healthcare settings. Few systems provide a comprehensive
attribute extraction for terminologies. While giant large language models
(LLMs) like GPT-4 and LLaMA 405B excel at structuring tasks, they are slow,
costly, and impractical for large-scale use. To overcome these limitations, we
introduce GENIE, a Generative Note Information Extraction system that leverages
LLMs to streamline the structuring of unstructured clinical text into usable
data with standardized format. GENIE processes entire paragraphs in a single
pass, extracting entities, assertion statuses, locations, modifiers, values,
and purposes with high accuracy. Its unified, end-to-end approach simplifies
workflows, reduces errors, and eliminates the need for extensive manual
intervention. Using a robust data preparation pipeline and fine-tuned small
scale LLMs, GENIE achieves competitive performance across multiple information
extraction tasks, outperforming traditional tools like cTAKES and MetaMap and
can handle extra attributes to be extracted. GENIE strongly enhances real-world
applicability and scalability in healthcare systems. By open-sourcing the model
and test data, we aim to encourage collaboration and drive further advancements
in EHR structurization.
