Automated Extraction of Substance Use Information from Clinical Texts.

Journal: AMIA ... Annual Symposium proceedings. AMIA Symposium
Published Date: Nov 5, 2015

Abstract

Within clinical discourse, social history (SH) includes important information about substance use (alcohol, drug, and nicotine use) as key risk factors for disease, disability, and mortality. In this study, we developed and evaluated a natural language processing (NLP) system for automated detection of substance use statements and extraction of substance use attributes (e.g., temporal and status) based on Stanford Typed Dependencies. The developed NLP system leveraged linguistic resources and domain knowledge from a multi-site social history study, Propbank and the MiPACQ corpus. The system attained F-scores of 89.8, 84.6 and 89.4 respectively for alcohol, drug, and nicotine use statement detection, as well as average F-scores of 82.1, 90.3, 80.8, 88.7, 96.6, and 74.5 respectively for extraction of attributes. Our results suggest that NLP systems can achieve good performance when augmented with linguistic resources and domain knowledge when applied to a wide breadth of substance use free text clinical notes.

Authors

  • Yan Wang
    College of Animal Science and Technology, Beijing University of Agriculture, Beijing, China.
  • Elizabeth S Chen
    Center for Clinical & Translational Science, University of Vermont, Burlington, VT; Department of Medicine, University of Vermont, Burlington, VT.
  • Serguei Pakhomov
    Institute for Health Informatics, University of Minnesota, Minneapolis, Minnesota, USA.
  • Elliot Arsoniadis
    Institute for Health Informatics, University of Minnesota, Minneapolis, MN; Department of Surgery, University of Minnesota, Minneapolis, MN.
  • Elizabeth W Carter
    Center for Clinical & Translational Science, University of Vermont, Burlington, VT.
  • Elizabeth Lindemann
    Institute for Health Informatics, University of Minnesota, Minneapolis, MN.
  • Indra Neil Sarkar
    Center for Clinical & Translational Science, University of Vermont, Burlington, VT; Department of Microbiology & Molecular Genetics, University of Vermont, Burlington, VT.
  • Genevieve B Melton
    Institute for Health Informatics, University of Minnesota, Minneapolis, Minnesota, USA.

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