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Related Experiment Video

Updated: Jun 26, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

SPELL: A scalable NLP method using regular expressions and large language models for clinical information extraction.

Ricardo Kleinlein1, David W Bates2, Carolyn Guan1

  • 1Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.

Computer Methods and Programs in Biomedicine
|June 24, 2026
PubMed
Summary

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SPELL, a novel NLP pipeline, efficiently extracts structured data from clinical notes using regex and LLMs. This method significantly speeds up data analysis compared to manual annotation and full-document processing.

Area of Science:

  • Natural Language Processing (NLP)
  • Machine Learning (ML)
  • Clinical Informatics

Background:

  • Electronic Health Records (EHRs) contain valuable unstructured clinical narratives.
  • Analyzing unstructured EHR data at scale is challenging for research and decision-making.
  • SPELL (Snippet-Primed rEgex LLM Pipeline) was developed to address this challenge.

Purpose of the Study:

  • To develop a scalable NLP workflow for extracting structured variables from large collections of clinical narratives.
  • To combine regular-expression-based snippet retrieval with locally hosted large language model (LLM) inference.
  • To enable accurate and computationally efficient extraction of information from unstructured EHR data.

Main Methods:

  • SPELL retrieves text snippets using task-specific regular expressions and applies LLM inference on these snippets.
Keywords:
Electronic health recordsLarge language modelsNatural language processingPhenotypingRegular expressions

Related Experiment Videos

Last Updated: Jun 26, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

  • All processing is conducted within institutional computing environments.
  • Accuracy and efficiency were evaluated on obstetric tasks (blood loss, estimated due date, HELLP syndrome) and a generalizability task (ventricular tachycardia detection).

    • Main Results:

    • SPELL processed 31 million clinical notes, demonstrating scalability.
    • Snippet-based inference reduced processing time by 71-87% compared to full-document LLM inference.
    • High accuracy was achieved: 98% for blood loss, 92% for estimated due date, and 94% F1 for HELLP syndrome.

    Conclusions:

    • A hybrid regex-snippet-LLM pipeline enables accurate and computationally efficient extraction from unstructured EHR narratives.
    • SPELL offers a scalable solution for leveraging valuable information within clinical notes.
    • This approach enhances the utility of EHR data for research and clinical decision support.