Automated information extraction from free-text medical documents for stroke key performance indicators: a pilot study

Stephen Bacchi, Sam Gluck, Simon Koblar, Jim Jannes, Timothy Kleinig

Research output: Contribution to journalArticlepeer-review

Abstract

Automated information extraction might be able to assist with the collection of stroke key performance indicators (KPI). The feasibility of using natural language processing for classification-based KPI and datetime field extraction was assessed. Using free-text discharge summaries, random forest models achieved high levels of performance in classification tasks (area under the receiver operator curve 0.95–1.00). The datetime field extraction method was successful in 29 of 43 (67.4%) cases. Further studies are indicated.

Original languageEnglish
Pages (from-to)315-317
Number of pages3
JournalInternal Medicine Journal
Volume52
Issue number2
DOIs
Publication statusPublished or Issued - 20 Feb 2022
Externally publishedYes

Keywords

  • key performance indicator
  • machine learning
  • natural language processing
  • random forest

ASJC Scopus subject areas

  • Internal Medicine

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