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Falls, Natural language processing, Nursing informatics, Open-access software, Word embedding



  1. Topaz, Maxim PhD, RN
  2. Murga, Ludmila PhD
  3. Bar-Bachar, Ofrit MSc, PT
  4. McDonald, Margaret MSW
  5. Bowles, Kathryn PhD, RN


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.