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Published on: July 13, 2019
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).
Abstract:
This study develops and evaluates an open-source software (called NimbleMiner) that allows clinicians to interact with word embedding models with a goal of creating lexicons of similar terms. As a case study, the system was used to identify similar terms for patient fall history from homecare visit notes (N = 1 149 586) extracted from a large US homecare agency. Several experiments with parameters of word embedding models were conducted to identify the most time-effective and high-quality model. Models with larger word window width sizes (n = 10) that present users with about 50 top potentially similar terms for each (true) term validated by the user were most effective. NimbleMiner can assist in building a thorough vocabulary of fall history terms in about 2 hours. For domains like nursing, this approach could offer a valuable tool for rapid lexicon enrichment and discovery.
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