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Related Experiment Video

Updated: Jan 20, 2026

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NimbleMiner: An Open-Source Nursing-Sensitive Natural Language Processing System Based on Word Embedding.

Maxim Topaz1, Ludmila Murga, Ofrit Bar-Bachar

  • 1Author Affiliations: School of Nursing, Columbia University, New York (Drs Topaz and Murga and Ms Bar-Bachar); Harvard Medical School & Brigham and Women's Hospital, Boston, MA (Dr Topaz); The Visiting Nurse Service of New York (Ms McDonald and Dr Bowles); and School of Nursing, University of Pennsylvania, Philadelphia (Dr Bowles).

Computers, Informatics, Nursing : CIN
|September 4, 2019
PubMed
Summary
This summary is machine-generated.

NimbleMiner, an open-source tool, aids clinicians in discovering similar terms for medical lexicons. It efficiently builds patient fall history vocabularies from clinical notes, enhancing medical data analysis.

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Area of Science:

  • Natural Language Processing
  • Clinical Informatics
  • Health Informatics

Background:

  • Lexicon creation for clinical terms is time-consuming.
  • Automating vocabulary discovery can improve clinical data analysis.
  • Existing methods may lack efficiency in identifying semantically similar terms.

Purpose of the Study:

  • To develop and evaluate NimbleMiner, an open-source software for creating clinical term lexicons.
  • To assess the effectiveness of word embedding models for identifying similar terms in clinical notes.
  • To optimize parameters for time-efficient and high-quality lexicon generation.

Main Methods:

  • Developed NimbleMiner, an open-source software utilizing word embedding models.
  • Conducted a case study using 1,149,586 homecare visit notes to identify terms related to patient fall history.
  • Experimented with various word embedding model parameters, focusing on window width and number of suggested terms.

Main Results:

  • NimbleMiner effectively identifies similar terms for clinical lexicons.
  • Word embedding models with larger window sizes (n=10) and presenting ~50 terms were most effective.
  • The system can build a comprehensive fall history vocabulary in approximately 2 hours.

Conclusions:

  • NimbleMiner offers a valuable tool for rapid lexicon enrichment in clinical domains like nursing.
  • Open-source software combined with word embeddings can significantly accelerate medical vocabulary discovery.
  • This approach enhances the potential for thorough analysis of clinical data.