Detection of Surgical Site Infection Utilizing Automated Feature Generation in Clinical Notes.

Journal: Journal of healthcare informatics research
Published Date: Nov 6, 2018

Abstract

Postsurgical complications (PSCs) are known as a deviation from the normal postsurgical course and categorized by severity and treatment requirements. Surgical site infection (SSI) is one of major PSCs and the most common healthcare-associated infection, resulting in increased length of hospital stay and cost. In this work, we proposed an automated way to generate keyword features using sublanguage analysis with heuristics to detect SSI from cohort in clinical notes and evaluated these keywords with medical experts. To further valid our approach, we also applied different machine learning algorithms on cohort using automatically generated keywords. The results showed that our approach was able to identify SSI keywords from clinical narratives and can be used as a foundation to develop an information extraction system or support search-based natural language processing (NLP) approaches by augmenting search queries.

Authors

  • Feichen Shen
    Department of Health Sciences Research, Rochester MN.
  • David W Larson
    Department of Surgery Mayo Clinic College of Medicine, Rochester MN.
  • James M Naessens
    Department of Health Sciences Research, Rochester MN.
  • Elizabeth B Habermann
    Department of Health Sciences Research, Rochester MN.
  • Hongfang Liu
    Department of Artificial Intelligence & Informatics, Mayo Clinic, Rochester, MN, United States.
  • Sunghwan Sohn
    Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, USA.

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