Exploration of critical care data by using unsupervised machine learning.

Journal: Computer methods and programs in biomedicine
Published Date: Apr 28, 2020

Abstract

BACKGROUND AND OBJECTIVE: Identification of subgroups may be useful to understand the clinical characteristics of ICU patients. The purposes of this study were to apply an unsupervised machine learning method to ICU patient data to discover subgroups among them; and to examine their clinical characteristics, therapeutic procedures conducted during the ICU stay, and discharge dispositions.

Authors

  • Sookyung Hyun
    College of Nursing, Pusan National University, 49 Busandaehak-ro Mulgeum-eup, Yangsan-si, 50612, South Korea. Electronic address: sookyung.hyun@pusan.ac.kr.
  • Pacharmon Kaewprag
    Department of Computer Engineering, Ramkhamhaeng University, Bangkok, Thailand.
  • Cheryl Cooper
    Central Quality and Education, The Ohio State University Wexner Medical Center, Ohio, United States.
  • Brenda Hixon
    Department of Health Services Nursing Education, The Ohio State University Wexner Medical Center, Ohio, United States.
  • Susan Moffatt-Bruce
    Department of Surgery, The Ohio State University Wexner Medical Center, Columbus, Ohio, United States.

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