Development of a natural language processing algorithm to detect chronic cough in electronic health records.

Journal: BMC pulmonary medicine
Published Date: Jun 28, 2022

Abstract

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.

Authors

  • Vishal Bali
    Center for Observational and Real-World Evidence, Merck Co., Inc, 2000 Galloping Hill Rd, Kenilworth, NJ, 07033 United States. Electronic address: vishal.bali@merck.com.
  • Jessica Weaver
    Center for Observational and Real-World Evidence, Merck Co., Inc, 2000 Galloping Hill Rd, Kenilworth, NJ, 07033 United States. Electronic address: jessica.weaver@merck.com.
  • Vladimir Turzhitsky
    Center for Observational and Real-World Evidence, Merck Co., Inc, 2000 Galloping Hill Rd, Kenilworth, NJ, 07033 United States. Electronic address: vladimir.turzhitsky@merck.com.
  • Jonathan Schelfhout
    Center for Observational and Real-World Evidence (CORE), Merck & Co., Inc., Rahway, NJ, USA.
  • Misti L Paudel
    Health Economics and Outcomes Research (HEOR), Optum Insight, Eden Prairie, MN, USA.
  • Erin Hulbert
    Health Economics and Outcomes Research (HEOR), Optum Insight, Eden Prairie, MN, USA.
  • Jesse Peterson-Brandt
    Health Economics and Outcomes Research (HEOR), Optum Insight, Eden Prairie, MN, USA.
  • Anne-Marie Guerra Currie
    Optum Enterprise Analytics (OEA), Optum Insight, Eden Prairie, MN, USA.
  • Dylan Bakka
    Optum Enterprise Analytics (OEA), Optum Insight, Eden Prairie, MN, USA.

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