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Using natural language processing and VetCompass to understand antimicrobial usage patterns in Australia
Author(s) -
Hur B,
Hardefeldt LY,
Verspoor K,
Baldwin T,
Gilkerson JR
Publication year - 2019
Publication title -
australian veterinary journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.382
H-Index - 59
eISSN - 1751-0813
pISSN - 0005-0423
DOI - 10.1111/avj.12836
Subject(s) - antimicrobial stewardship , medical record , antimicrobial , stewardship (theology) , computer science , medicine , artificial intelligence , natural language processing , data science , biology , political science , antibiotic resistance , politics , law , microbiology and biotechnology , antibiotics
Background Currently there is an incomplete understanding of antimicrobial usage patterns in veterinary clinics in Australia, but such knowledge is critical for the successful implementation and monitoring of antimicrobial stewardship programs. Methods VetCompass Australia collects medical records from 181 clinics in Australia (as of May 2018). These records contain detailed information from individual consultations regarding the medications dispensed. One unique aspect of VetCompass Australia is its focus on applying natural language processing (NLP) and machine learning techniques to analyse the records, similar to efforts conducted in other medical studies. Results The free text fields of 4,394,493 veterinary consultation records of dogs and cats between 2013 and 2018 were collated by VetCompass Australia and NLP techniques applied to enable the querying of the antimicrobial usage within these consultations. Conclusion The NLP algorithms developed matched antimicrobial in clinical records with 96.7% accuracy and an F1 Score of 0.85, as evaluated relative to expert annotations. This dataset can be readily queried to demonstrate the antimicrobial usage patterns of companion animal practices throughout Australia.