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Development of a Natural Language Processing Algorithm to Detect Chronic Cough in Electronic Health Records by Vishal Bali; Jessica Weaver; Vladimir Turzhitsky; Jonathan Schelfhout; Misti L. Paudel; Erin Hulbert; Jesse Peterson-Brandt; Anne-Marie Guerra Currie; Dylan Bakka is a Medicine article available to read on EtoBox.
What is Development of a Natural Language Processing Algorithm to Detect Chronic Cough in Electronic Health Records about?
## Background Chronic cough (CC) is difficult to identify in electronic health records (EHRs) due to the lack of specific diagnostic codes. We developed a natural language processing (NLP) model to identify cough in free-text provider notes in EHRs from multiple health care providers with the objective of using the model in a rules-based CC algorithm to identify individuals with CC from EHRs and to describe the demographic and clinical characteristics of individuals with CC. ## Methods This was a retrospective observational study of enrollees in Optum’s Integrated Clinical + Claims Database. Participants were 18–85 years of age with medical and pharmacy health insurance coverage between January 2016 and March 2017. A labeled reference standard data set was constructed by manually annotating 1000 randomly selected provider notes from the EHRs of enrollees with ≥ 1 cough mention. An NLP model was developed to extract positive or negated cough contexts. NLP, cough diagnosis and medications identified cough encounters. Patients with ≥ 3 encounters spanning at least 56 days within 120 days were defined as having CC. ## Results The positive predictive value and sensitivity of the NLP alg
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- Author
- Vishal Bali; Jessica Weaver; Vladimir Turzhitsky; Jonathan Schelfhout; Misti L. Paudel; Erin Hulbert; Jesse Peterson-Brandt; Anne-Marie Guerra Currie; Dylan Bakka
- Publisher
- Springer Science and Business Media LLC
- Published
- 2022
- Language
- EN
- Field
- Medicine (Health Sciences)