Augmented intelligence with natural language processing applied to electronic health records for identifying patients with non-alcoholic fatty liver disease at risk for disease progression.

Journal: International journal of medical informatics
Published Date: Jul 6, 2019

Abstract

OBJECTIVE: Electronic health record (EHR) systems contain structured data (such as diagnostic codes) and unstructured data (clinical documentation). Clinical insights can be derived from analyzing both. The use of natural language processing (NLP) algorithms to effectively analyze unstructured data has been well demonstrated. Here we examine the utility of NLP for the identification of patients with non-alcoholic fatty liver disease, assess patterns of disease progression, and identify gaps in care related to breakdown in communication among providers.

Authors

  • Tielman T Van Vleck
    The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, USA. Electronic address: tielman.vanvleck@mssm.edu.
  • Lili Chan
    Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, USA.
  • Steven G Coca
    Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, USA.
  • Catherine K Craven
    Institute for Healthcare Delivery Science, Dept. of Pop. Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, USA; Clinical Informatics Group, IT Department, Mount Sinai Health System, New York, USA.
  • Ron Do
    The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, USA.
  • Stephen B Ellis
    Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Joseph L Kannry
    Information Technology, Mount Sinai Medical Center, New York, USA.
  • Ruth J F Loos
    The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, USA.
  • Peter A Bonis
    Division of Gastroenterology, Tufts Medical Center, Boston, USA.
  • Judy Cho
    The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, USA; Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, USA; Division of Gastroenterology, Icahn School of Medicine at Mount Sinai, New York, USA.
  • Girish N Nadkarni
    Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY, USA.

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