Automatically identifying social isolation from clinical narratives for patients with prostate Cancer.

Journal: BMC medical informatics and decision making
Published Date: Mar 14, 2019

Abstract

BACKGROUND: Social isolation is an important social determinant that impacts health outcomes and mortality among patients. The National Academy of Medicine recently recommended that social isolation be documented in electronic health records (EHR). However, social isolation usually is not recorded or obtained as coded data but rather collected from patient self-report or documented in clinical narratives. This study explores the feasibility and effectiveness of natural language processing (NLP) strategy for identifying patients who are socially isolated from clinical narratives.

Authors

  • Vivienne J Zhu
    Biomedical Informatics Center at Medical University of South Carolina, Charleston, South Carolina.
  • Leslie A Lenert
    Biomedical Informatics Center, Medical University of South Carolina, Charleston, SC 29425, United States.
  • Brian E Bunnell
    Biomedical Informatics Center at Medical University of South Carolina, Chalrleston, South Carolina, USA.
  • Jihad S Obeid
    Biomedical Informatics Center, Medical University of South Carolina, Charleston, SC 29425, United States.
  • Melanie Jefferson
    Holling Cancer Center and Department of Psychiatry and Behavioral Sciences at Medical University of South Carolina, Charleston, South Carolina, USA.
  • Chanita Hughes Halbert
    Holling Cancer Center and Department of Psychiatry and Behavioral Sciences at Medical University of South Carolina, Charleston, South Carolina, USA.

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