Evaluating the Portability of an NLP System for Processing Echocardiograms: A Retrospective, Multi-site Observational Study

AMIA Annu Symp Proc. 2020 Mar 4:2019:190-199. eCollection 2019.


While natural language processing (NLP) of unstructured clinical narratives holds the potential for patient care and clinical research, portability of NLP approaches across multiple sites remains a major challenge. This study investigated the portability of an NLP system developed initially at the Department of Veterans Affairs (VA) to extract 27 key cardiac concepts from free-text or semi-structured echocardiograms from three academic edical centers: Weill Cornell Medicine, Mayo Clinic and Northwestern Medicine. While the NLP system showed high precision and recall easurements for four target concepts (aortic valve regurgitation, left atrium size at end systole, mitral valve regurgitation, tricuspid valve regurgitation) across all sites, we found moderate or poor results for the remaining concepts and the NLP system performance varied between individual sites.

Publication types

  • Multicenter Study
  • Observational Study
  • Research Support, N.I.H., Extramural

MeSH terms

  • Echocardiography*
  • Electronic Health Records*
  • Health Information Interoperability*
  • Heart / anatomy & histology
  • Heart / diagnostic imaging
  • Heart Valve Diseases / diagnostic imaging*
  • Heart Valve Diseases / physiopathology
  • Humans
  • Narration
  • Natural Language Processing*
  • Retrospective Studies