Supporting the use of standardized nursing terminologies with automatic subject heading prediction: a comparison of sentence-level text classification methods.

Journal: Journal of the American Medical Informatics Association : JAMIA
PMID: 31605490

Abstract

OBJECTIVE: This study focuses on the task of automatically assigning standardized (topical) subject headings to free-text sentences in clinical nursing notes. The underlying motivation is to support nurses when they document patient care by developing a computer system that can assist in incorporating suitable subject headings that reflect the documented topics. Central in this study is performance evaluation of several text classification methods to assess the feasibility of developing such a system.

Authors

  • Hans Moen
    Turku NLP Group, Department of Future Technologies, University of Turku, Finland.
  • Kai Hakala
    Department of Information Technology, University of Turku, Turku, Finland ; The University of Turku Graduate School (UTUGS), University of Turku, Turku, Finland.
  • Laura-Maria Peltonen
    Nursing Science, University of Turku, and Turku University Hospital, Turku, Finland.
  • Henry Suhonen
    Department of Nursing Science, University of Turku, Finland.
  • Filip Ginter
    Department of Information Technology, University of Turku, Turku, Finland.
  • Tapio Salakoski
    TurkuNLP group, Department of Future Technologies, University of Turku, Turku, Finland.
  • Sanna Salanterä
    Nursing Science, University of Turku, and Turku University Hospital, Turku, Finland.

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