Key result
A natural language processing algorithm ascertained peripheral arterial disease status from clinical notes with high sensitivity (0.96), positive predictive value (0.92), and specificity (0.98).
Why the study?
Does a natural language processing algorithm accurately ascertain peripheral arterial disease status from clinical notes compared to manual chart review?
Does a natural language processing algorithm accurately ascertain peripheral arterial disease status from clinical notes compared to manual chart review?
An NLP algorithm can accurately and efficiently identify peripheral arterial disease cases from clinical notes, performing comparably to expert manual chart review.
May enable scalable PAD phenotyping in EHRs; leaves open prospective validation before clinical use.
Peripheral arterial disease (PAD) is a chronic disease that affects millions of people worldwide. Ascertaining PAD status from clinical notes by manual chart review is labor intensive and time consuming. In this paper, we describe a natural language processing (NLP) algorithm for automated ascertainment of PAD status from clinical notes using predetermined criteria. We developed and evaluated our system against a gold standard that was created by medical experts based on manual chart review. Our system ascertained PAD status from clinical notes with high sensitivity (0.96), positive predictive value (0.92), negative predictive value (0.99) and specificity (0.98). NLP approaches can be used for rapid, efficient and automated ascertainment of PAD cases with implications for patient care and epidemiologic research.
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Afzal et al. (2016) studied Peripheral arterial disease (PAD). Natural language processing (NLP) algorithm vs. Manual chart review by medical experts was evaluated on Ascertainment of PAD status. A natural language processing algorithm ascertained peripheral arterial disease status from clinical notes with high sensitivity (0.96), positive predictive value (0.92), and specificity (0.98).
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