Development and application of a high throughput natural language processing architecture to convert all clinical documents in a clinical data warehouse into standardized medical vocabularies.

Journal: Journal of the American Medical Informatics Association : JAMIA
Published Date: Nov 1, 2019

Abstract

OBJECTIVE: Natural language processing (NLP) engines such as the clinical Text Analysis and Knowledge Extraction System are a solution for processing notes for research, but optimizing their performance for a clinical data warehouse remains a challenge. We aim to develop a high throughput NLP architecture using the clinical Text Analysis and Knowledge Extraction System and present a predictive model use case.

Authors

  • Majid Afshar
    Loyola University Chicago, Chicago, IL.
  • Dmitriy Dligach
    Department of Public Health Sciences, Stritch School of Medicine, Loyola University Chicago, Maywood, IL.
  • Brihat Sharma
    Department of Computer Science, Loyola University Chicago, Chicago, IL, USA.
  • Xiaoyuan Cai
    Informatics and Systems Development, Health Sciences Division, Loyola University Chicago, Maywood, Illinois, USA.
  • Jason Boyda
    Informatics and Systems Development, Health Sciences Division, Loyola University Chicago, Maywood, Illinois, USA.
  • Steven Birch
    Informatics and Systems Development, Health Sciences Division, Loyola University Chicago, Maywood, Illinois, USA.
  • Daniel Valdez
    Informatics and Systems Development, Health Sciences Division, Loyola University Chicago, Maywood, Illinois, USA.
  • Suzan Zelisko
    Informatics and Systems Development, Health Sciences Division, Loyola University Chicago, Maywood, Illinois, USA.
  • Cara Joyce
    Loyola University Chicago, Chicago, IL.
  • François Modave
    Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, 2004 Mowry Road, Gainesville, FL, 32610, USA.
  • Ron Price
    Informatics and Systems Development, Health Sciences Division, Loyola University Chicago, Maywood, IL.

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