In-hospital fall prediction using machine learning algorithms and the Morse fall scale in patients with acute stroke: a nested case-control study.

Journal: BMC medical informatics and decision making
Published Date:

Abstract

BACKGROUND: Falls are one of the most common accidents in medical institutions, which can threaten the safety of inpatients and negatively affect their prognosis. Herein, we developed a machine learning (ML) model for fall prediction in patients with acute stroke and compared its accuracy with that of the existing fall risk prediction tool, the Morse Fall Scale (MFS).

Authors

  • Jun Hwa Choi
    College of Nursing, Kyungpook National University, 680 Gukchaebosang-ro, Jung-gu, Daegu, 41944, Republic of Korea.
  • Eun Suk Choi
    College of Nursing, Kyungpook National University, 680 Gukchaebosang-ro, Jung-gu, Daegu, 41944, Republic of Korea. eschoi2007@knu.ac.kr.
  • Dougho Park
    Department of Rehabilitation Medicine, Pohang Stroke and Spine Hospital, Pohang, Republic of Korea.