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

Research output: Contribution to journalArticle

5 Scopus citations

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 validate 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.

Original languageEnglish (US)
Pages (from-to)267-282
Number of pages16
JournalJournal of Healthcare Informatics Research
Volume3
Issue number3
DOIs
StatePublished - Sep 15 2019

Keywords

  • Feature generation
  • Machine learning
  • Natural language processing
  • Postsurgical complication

ASJC Scopus subject areas

  • Health Informatics
  • Computer Science Applications
  • Information Systems
  • Artificial Intelligence

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