Accepted for/Published in: JMIR Medical Informatics
Date Submitted: Apr 21, 2021
Date Accepted: Dec 4, 2021
Identification of prediabetes discussions in unstructured clinical documentation using natural language processing methods
ABSTRACT
Background:
Prediabetes affects 1 in 3 US adults. Most are not receiving evidence-based interventions so understanding how providers discuss prediabetes with patients will inform how to improve their care.
Objective:
Develop an NLP algorithm using machine learning techniques to identify discussions of prediabetes in narrative documentation.
Methods:
We developed and applied a keyword search strategy to identify discussions of prediabetes in clinical documentation for patients with prediabetes. We manually reviewed matching notes to determine which represented actual prediabetes discussions. We applied seven machine learning models against our manual annotation.
Results:
Machine learning classifiers were able to achieve classification results that were close to human performance with up to 98% precision and recall to identify prediabetes discussions in clinical documentation.
Conclusions:
We demonstrated that prediabetes discussions can be accurately identified using an NLP algorithm. This approach can be used to understand and identify prediabetes management practices in primary care, thereby informing interventions to improve guideline-concordant care.
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Copyright
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